{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:gray\">\n",
    "Copyright &copy; 2020-2021 by Fraunhofer-Gesellschaft. All rights reserved.<br>\n",
    "Fraunhofer Institute for Integrated Circuits IIS, Division Engineering of Adaptive Systems EAS<br>\n",
    "Münchner Straße 16, 01187 Dresden, Germany\n",
    "</span>\n",
    "\n",
    "---\n",
    "\n",
    "## ESB - Energy Saving by Blockchain\n",
    "\n",
    "Eurostars – EXP 00119832 / EUS-2019113348\n",
    "\n",
    "---\n",
    "\n",
    "## Prediction of Energy Consumption for Variable Customer Portfolios Including Aleatoric Uncertainty Estimation\n",
    "\n",
    "*Oliver Mey, André Schneider, Olaf Enge-Rosenblatt, Yesnier Bravo, Pit Stenzel*\n",
    "\n",
    "The notebook is part of a paper submission contributed to the **10th International Conference on Power Science and Engineering (ICPSE 2021)** will be held on Oct. 21-23, 2021 in Yildiz Technical University, Istanbul, Turkey.\n",
    "\n",
    "---\n",
    "\n",
    "# A1: Data Exploration\n",
    "\n",
    "This notebook loads the available datasets and explains the content and how the data is structured. Furthermore, a few sample plots illustrate the data.\n",
    "\n",
    "---\n",
    "\n",
    "<span style=\"color:gray\">\n",
    "Version 0.3.7 (September 12, 2021)<br>\n",
    "Authors: Oliver Mey, André Schneider (Fraunhofer IIS)<br>\n",
    "</span>    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings, logging, os\n",
    "warnings.filterwarnings('ignore')\n",
    "logging.disable(logging.WARNING)\n",
    "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import joblib\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import holidays as hd\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set(rc={'figure.figsize':(16, 6)})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '..'\n",
    "categories = ['consumption', 'weather', 'profiles']\n",
    "files = [path + '/data/' + '20201015_' + name + '.xlsx' for name in categories]\n",
    "timezone = 'Europe/Madrid'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function Definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fix_DST(data):\n",
    "    data = data[~data.index.duplicated(keep='first')]\n",
    "    data = data.resample('H').ffill()\n",
    "    return data\n",
    "\n",
    "def crop(data):\n",
    "    hour_index = data.index.hour\n",
    "    t0 = data[hour_index==0].head(1).index\n",
    "    tn = data[hour_index==23].tail(1).index\n",
    "    data.drop(data.loc[data.index < t0[0]].index, inplace=True)\n",
    "    data.drop(data.loc[data.index > tn[0]].index, inplace=True)\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Consumption Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "consumptions = pd.read_excel(files[categories.index('consumption')], parse_dates=[0], index_col=0)\n",
    "consumptions.columns = pd.DataFrame(consumptions.columns, columns=['customer']).index\n",
    "consumptions.index.name = 'time'\n",
    "fix_DST(consumptions)\n",
    "crop(consumptions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *consumptions* dataframe contains **499** columns. A column represents a single *customer* with a customer ID in the range of *0..498*. Each row in the dataframe contains the *consumption* values for a certain timestamp within the measurement period of about one year (Jan 01 - Dec 31, 2019). The time series were recorded with an hourly resolution. This means the dataframe contains **8760** rows (365 day x 24 hours)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>489</th>\n",
       "      <th>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:00:00</th>\n",
       "      <td>0.039</td>\n",
       "      <td>0.384</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.304</td>\n",
       "      <td>0.073</td>\n",
       "      <td>3.262</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.645</td>\n",
       "      <td>1.279</td>\n",
       "      <td>...</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.488</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.366</td>\n",
       "      <td>0.290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 01:00:00</th>\n",
       "      <td>0.269</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.846</td>\n",
       "      <td>2.210</td>\n",
       "      <td>0.673</td>\n",
       "      <td>0.047</td>\n",
       "      <td>3.252</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.746</td>\n",
       "      <td>0.851</td>\n",
       "      <td>...</td>\n",
       "      <td>0.447</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.232</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.447</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.308</td>\n",
       "      <td>0.369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 02:00:00</th>\n",
       "      <td>0.331</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.970</td>\n",
       "      <td>1.797</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.058</td>\n",
       "      <td>3.043</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.678</td>\n",
       "      <td>0.789</td>\n",
       "      <td>...</td>\n",
       "      <td>0.439</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.508</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 03:00:00</th>\n",
       "      <td>0.093</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.803</td>\n",
       "      <td>1.010</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.059</td>\n",
       "      <td>2.960</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.644</td>\n",
       "      <td>0.637</td>\n",
       "      <td>...</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.201</td>\n",
       "      <td>0.358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 04:00:00</th>\n",
       "      <td>0.116</td>\n",
       "      <td>0.050</td>\n",
       "      <td>1.303</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0.072</td>\n",
       "      <td>3.054</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.262</td>\n",
       "      <td>...</td>\n",
       "      <td>1.256</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 19:00:00</th>\n",
       "      <td>0.009</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.773</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.579</td>\n",
       "      <td>0.322</td>\n",
       "      <td>3.851</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.303</td>\n",
       "      <td>...</td>\n",
       "      <td>0.039</td>\n",
       "      <td>1.308</td>\n",
       "      <td>0.009</td>\n",
       "      <td>1.761</td>\n",
       "      <td>0.011</td>\n",
       "      <td>2.036</td>\n",
       "      <td>0.249</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 20:00:00</th>\n",
       "      <td>0.009</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0.738</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.229</td>\n",
       "      <td>1.810</td>\n",
       "      <td>3.767</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.053</td>\n",
       "      <td>5.062</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.011</td>\n",
       "      <td>2.022</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.363</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 21:00:00</th>\n",
       "      <td>0.010</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.748</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.107</td>\n",
       "      <td>1.478</td>\n",
       "      <td>3.342</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.108</td>\n",
       "      <td>2.140</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.010</td>\n",
       "      <td>1.107</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.573</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 22:00:00</th>\n",
       "      <td>0.009</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.873</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.696</td>\n",
       "      <td>3.084</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.730</td>\n",
       "      <td>...</td>\n",
       "      <td>2.799</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.371</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:00:00</th>\n",
       "      <td>0.010</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.769</td>\n",
       "      <td>1.001</td>\n",
       "      <td>0.683</td>\n",
       "      <td>1.017</td>\n",
       "      <td>2.910</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.646</td>\n",
       "      <td>...</td>\n",
       "      <td>2.483</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.404</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 499 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       0      1      2      3      4      5      6    7    \\\n",
       "time                                                                        \n",
       "2019-01-01 00:00:00  0.039  0.384  0.986  0.706  0.304  0.073  3.262  3.0   \n",
       "2019-01-01 01:00:00  0.269  0.051  0.846  2.210  0.673  0.047  3.252  4.0   \n",
       "2019-01-01 02:00:00  0.331  0.049  0.970  1.797  0.310  0.058  3.043  4.0   \n",
       "2019-01-01 03:00:00  0.093  0.049  0.803  1.010  0.545  0.059  2.960  4.0   \n",
       "2019-01-01 04:00:00  0.116  0.050  1.303  0.660  0.410  0.072  3.054  3.0   \n",
       "...                    ...    ...    ...    ...    ...    ...    ...  ...   \n",
       "2019-12-31 19:00:00  0.009  0.052  0.773  0.138  0.579  0.322  3.851  2.0   \n",
       "2019-12-31 20:00:00  0.009  0.410  0.738  0.076  0.229  1.810  3.767  1.0   \n",
       "2019-12-31 21:00:00  0.010  0.058  0.748  0.072  0.107  1.478  3.342  1.0   \n",
       "2019-12-31 22:00:00  0.009  0.055  0.873  0.090  0.071  0.696  3.084  2.0   \n",
       "2019-12-31 23:00:00  0.010  0.056  0.769  1.001  0.683  1.017  2.910  2.0   \n",
       "\n",
       "                       8      9    ...    489    490    491    492    493  \\\n",
       "time                               ...                                      \n",
       "2019-01-01 00:00:00  0.645  1.279  ...  0.431  0.100  0.010  0.258  0.009   \n",
       "2019-01-01 01:00:00  0.746  0.851  ...  0.447  0.025  0.009  0.232  0.009   \n",
       "2019-01-01 02:00:00  0.678  0.789  ...  0.439  0.025  0.009  0.260  0.009   \n",
       "2019-01-01 03:00:00  0.644  0.637  ...  0.913  0.023  0.009  0.252  0.009   \n",
       "2019-01-01 04:00:00  0.130  0.262  ...  1.256  0.024  0.009  0.278  0.009   \n",
       "...                    ...    ...  ...    ...    ...    ...    ...    ...   \n",
       "2019-12-31 19:00:00  0.236  0.303  ...  0.039  1.308  0.009  1.761  0.011   \n",
       "2019-12-31 20:00:00  0.053  5.062  ...  0.036  0.298  0.027  0.375  0.011   \n",
       "2019-12-31 21:00:00  0.108  2.140  ...  0.261  0.120  0.052  0.463  0.010   \n",
       "2019-12-31 22:00:00  0.067  0.730  ...  2.799  0.117  0.051  0.371  0.011   \n",
       "2019-12-31 23:00:00  0.054  0.646  ...  2.483  0.111  0.051  0.435  0.011   \n",
       "\n",
       "                       494    495    496    497    498  \n",
       "time                                                    \n",
       "2019-01-01 00:00:00  0.488  0.810  0.309  0.366  0.290  \n",
       "2019-01-01 01:00:00  0.447  0.753  0.381  0.308  0.369  \n",
       "2019-01-01 02:00:00  0.508  0.092  0.193  0.137  0.292  \n",
       "2019-01-01 03:00:00  0.511  0.032  0.124  0.201  0.358  \n",
       "2019-01-01 04:00:00  0.510  0.081  0.081  0.223  0.380  \n",
       "...                    ...    ...    ...    ...    ...  \n",
       "2019-12-31 19:00:00  2.036  0.249  0.223  0.245  0.573  \n",
       "2019-12-31 20:00:00  2.022  0.058  0.363  0.354  0.644  \n",
       "2019-12-31 21:00:00  1.107  0.011  0.573  0.393  0.635  \n",
       "2019-12-31 22:00:00  0.380  0.039  0.431  0.429  0.509  \n",
       "2019-12-31 23:00:00  0.404  0.072  0.300  0.309  0.419  \n",
       "\n",
       "[8760 rows x 499 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>489</th>\n",
       "      <th>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.283303</td>\n",
       "      <td>0.263868</td>\n",
       "      <td>1.750522</td>\n",
       "      <td>0.275184</td>\n",
       "      <td>0.277605</td>\n",
       "      <td>0.218282</td>\n",
       "      <td>2.393745</td>\n",
       "      <td>4.763017</td>\n",
       "      <td>0.227725</td>\n",
       "      <td>0.602395</td>\n",
       "      <td>...</td>\n",
       "      <td>0.269928</td>\n",
       "      <td>0.170956</td>\n",
       "      <td>0.053764</td>\n",
       "      <td>0.803501</td>\n",
       "      <td>0.169984</td>\n",
       "      <td>0.809818</td>\n",
       "      <td>0.112445</td>\n",
       "      <td>0.238333</td>\n",
       "      <td>0.413233</td>\n",
       "      <td>0.460819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.436013</td>\n",
       "      <td>0.345561</td>\n",
       "      <td>0.781109</td>\n",
       "      <td>0.332088</td>\n",
       "      <td>0.387865</td>\n",
       "      <td>0.248986</td>\n",
       "      <td>1.648356</td>\n",
       "      <td>8.413416</td>\n",
       "      <td>0.310379</td>\n",
       "      <td>0.783770</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363086</td>\n",
       "      <td>0.308614</td>\n",
       "      <td>0.048554</td>\n",
       "      <td>0.824487</td>\n",
       "      <td>0.673361</td>\n",
       "      <td>0.573233</td>\n",
       "      <td>0.112697</td>\n",
       "      <td>0.146475</td>\n",
       "      <td>0.371725</td>\n",
       "      <td>0.127375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041000</td>\n",
       "      <td>0.118000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.172000</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076000</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>0.214000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.062000</td>\n",
       "      <td>1.143750</td>\n",
       "      <td>0.089000</td>\n",
       "      <td>0.091000</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>1.133750</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.061000</td>\n",
       "      <td>0.234000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054000</td>\n",
       "      <td>0.023000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.317750</td>\n",
       "      <td>0.026000</td>\n",
       "      <td>0.422000</td>\n",
       "      <td>0.038000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.149000</td>\n",
       "      <td>0.359000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.113000</td>\n",
       "      <td>0.094000</td>\n",
       "      <td>1.611000</td>\n",
       "      <td>0.128000</td>\n",
       "      <td>0.105000</td>\n",
       "      <td>0.128000</td>\n",
       "      <td>2.478500</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.326000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.024000</td>\n",
       "      <td>0.053000</td>\n",
       "      <td>0.456000</td>\n",
       "      <td>0.054000</td>\n",
       "      <td>0.592000</td>\n",
       "      <td>0.071000</td>\n",
       "      <td>0.206500</td>\n",
       "      <td>0.295000</td>\n",
       "      <td>0.467000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.277000</td>\n",
       "      <td>0.387250</td>\n",
       "      <td>2.112250</td>\n",
       "      <td>0.332000</td>\n",
       "      <td>0.266000</td>\n",
       "      <td>0.268000</td>\n",
       "      <td>3.495000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.245000</td>\n",
       "      <td>0.606000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.335250</td>\n",
       "      <td>0.122000</td>\n",
       "      <td>0.084000</td>\n",
       "      <td>1.035250</td>\n",
       "      <td>0.056000</td>\n",
       "      <td>0.976000</td>\n",
       "      <td>0.151000</td>\n",
       "      <td>0.343000</td>\n",
       "      <td>0.473000</td>\n",
       "      <td>0.551000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.067000</td>\n",
       "      <td>3.192000</td>\n",
       "      <td>4.899000</td>\n",
       "      <td>3.547000</td>\n",
       "      <td>3.342000</td>\n",
       "      <td>2.487000</td>\n",
       "      <td>8.994000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>4.185000</td>\n",
       "      <td>8.691000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.149000</td>\n",
       "      <td>2.115000</td>\n",
       "      <td>1.193000</td>\n",
       "      <td>7.760000</td>\n",
       "      <td>17.114000</td>\n",
       "      <td>4.769000</td>\n",
       "      <td>1.046000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>2.030000</td>\n",
       "      <td>1.309000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 499 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0            1            2            3            4    \\\n",
       "count  8760.000000  8760.000000  8760.000000  8760.000000  8760.000000   \n",
       "mean      0.283303     0.263868     1.750522     0.275184     0.277605   \n",
       "std       0.436013     0.345561     0.781109     0.332088     0.387865   \n",
       "min       0.000000     0.041000     0.000000     0.007000     0.008000   \n",
       "25%       0.000000     0.062000     1.143750     0.089000     0.091000   \n",
       "50%       0.113000     0.094000     1.611000     0.128000     0.105000   \n",
       "75%       0.277000     0.387250     2.112250     0.332000     0.266000   \n",
       "max       3.067000     3.192000     4.899000     3.547000     3.342000   \n",
       "\n",
       "               5            6            7            8            9    ...  \\\n",
       "count  8760.000000  8760.000000  8760.000000  8760.000000  8760.000000  ...   \n",
       "mean      0.218282     2.393745     4.763017     0.227725     0.602395  ...   \n",
       "std       0.248986     1.648356     8.413416     0.310379     0.783770  ...   \n",
       "min       0.040000     0.012000     0.000000     0.041000     0.118000  ...   \n",
       "25%       0.079000     1.133750     2.000000     0.061000     0.234000  ...   \n",
       "50%       0.128000     2.478500     3.000000     0.140000     0.326000  ...   \n",
       "75%       0.268000     3.495000     4.000000     0.245000     0.606000  ...   \n",
       "max       2.487000     8.994000   107.000000     4.185000     8.691000  ...   \n",
       "\n",
       "               489          490          491          492          493  \\\n",
       "count  8760.000000  8760.000000  8760.000000  8760.000000  8760.000000   \n",
       "mean      0.269928     0.170956     0.053764     0.803501     0.169984   \n",
       "std       0.363086     0.308614     0.048554     0.824487     0.673361   \n",
       "min       0.000000     0.016000     0.008000     0.172000     0.007000   \n",
       "25%       0.054000     0.023000     0.010000     0.317750     0.026000   \n",
       "50%       0.100000     0.024000     0.053000     0.456000     0.054000   \n",
       "75%       0.335250     0.122000     0.084000     1.035250     0.056000   \n",
       "max       3.149000     2.115000     1.193000     7.760000    17.114000   \n",
       "\n",
       "               494          495          496          497          498  \n",
       "count  8760.000000  8760.000000  8760.000000  8760.000000  8760.000000  \n",
       "mean      0.809818     0.112445     0.238333     0.413233     0.460819  \n",
       "std       0.573233     0.112697     0.146475     0.371725     0.127375  \n",
       "min       0.160000     0.000000     0.076000     0.079000     0.214000  \n",
       "25%       0.422000     0.038000     0.106000     0.149000     0.359000  \n",
       "50%       0.592000     0.071000     0.206500     0.295000     0.467000  \n",
       "75%       0.976000     0.151000     0.343000     0.473000     0.551000  \n",
       "max       4.769000     1.046000     1.040000     2.030000     1.309000  \n",
       "\n",
       "[8 rows x 499 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consumptions.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example Plot for Consumption of Customer #20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "customer = 20\n",
    "_ = consumptions.loc[:, customer].plot(color='b', alpha=0.7, ylabel='energy consumption in kWh per hour',\n",
    "                                 title='Consumption of Customer #' + str(customer))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Weather Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather = pd.read_excel(files[categories.index('weather')], parse_dates=[0], index_col=0)\n",
    "weather.columns = consumptions.columns\n",
    "weather.index.name = 'time'\n",
    "fix_DST(weather)\n",
    "crop(weather)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *weather* dataframe contains **499** columns. A column represents a single *customer* with a customer ID in the range of *0..498*. Each row in the dataframe contains the *weather* data (outside temperature of the customer's region) for a certain timestamp within the measurement period of about one year (Jan 01 - Dec 30, 2019). The time series were recorded with an hourly resolution. This means the dataframe contains **8760** rows (365 day x 24 hours)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>489</th>\n",
       "      <th>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:00:00</th>\n",
       "      <td>10.31</td>\n",
       "      <td>10.35</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.70</td>\n",
       "      <td>11.53</td>\n",
       "      <td>10.76</td>\n",
       "      <td>10.57</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.66</td>\n",
       "      <td>10.49</td>\n",
       "      <td>...</td>\n",
       "      <td>10.40</td>\n",
       "      <td>9.53</td>\n",
       "      <td>9.94</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.14</td>\n",
       "      <td>3.20</td>\n",
       "      <td>2.63</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 01:00:00</th>\n",
       "      <td>10.34</td>\n",
       "      <td>10.38</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.37</td>\n",
       "      <td>11.78</td>\n",
       "      <td>10.79</td>\n",
       "      <td>10.61</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.33</td>\n",
       "      <td>10.73</td>\n",
       "      <td>...</td>\n",
       "      <td>10.65</td>\n",
       "      <td>10.21</td>\n",
       "      <td>10.63</td>\n",
       "      <td>3.70</td>\n",
       "      <td>3.65</td>\n",
       "      <td>3.71</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 02:00:00</th>\n",
       "      <td>10.36</td>\n",
       "      <td>10.40</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.05</td>\n",
       "      <td>12.03</td>\n",
       "      <td>10.82</td>\n",
       "      <td>10.64</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>1.01</td>\n",
       "      <td>10.98</td>\n",
       "      <td>...</td>\n",
       "      <td>10.90</td>\n",
       "      <td>10.88</td>\n",
       "      <td>11.33</td>\n",
       "      <td>4.22</td>\n",
       "      <td>4.17</td>\n",
       "      <td>4.23</td>\n",
       "      <td>1.53</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 03:00:00</th>\n",
       "      <td>10.39</td>\n",
       "      <td>10.43</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.73</td>\n",
       "      <td>12.28</td>\n",
       "      <td>10.84</td>\n",
       "      <td>10.68</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>0.69</td>\n",
       "      <td>11.23</td>\n",
       "      <td>...</td>\n",
       "      <td>11.14</td>\n",
       "      <td>11.56</td>\n",
       "      <td>12.02</td>\n",
       "      <td>4.74</td>\n",
       "      <td>4.69</td>\n",
       "      <td>4.75</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 04:00:00</th>\n",
       "      <td>10.27</td>\n",
       "      <td>10.31</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.51</td>\n",
       "      <td>12.13</td>\n",
       "      <td>10.73</td>\n",
       "      <td>10.57</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>0.48</td>\n",
       "      <td>11.06</td>\n",
       "      <td>...</td>\n",
       "      <td>10.97</td>\n",
       "      <td>11.33</td>\n",
       "      <td>11.79</td>\n",
       "      <td>4.47</td>\n",
       "      <td>4.42</td>\n",
       "      <td>4.48</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 19:00:00</th>\n",
       "      <td>17.99</td>\n",
       "      <td>18.03</td>\n",
       "      <td>14.88</td>\n",
       "      <td>14.94</td>\n",
       "      <td>17.51</td>\n",
       "      <td>18.15</td>\n",
       "      <td>18.08</td>\n",
       "      <td>12.33</td>\n",
       "      <td>14.89</td>\n",
       "      <td>16.91</td>\n",
       "      <td>...</td>\n",
       "      <td>16.84</td>\n",
       "      <td>16.00</td>\n",
       "      <td>16.10</td>\n",
       "      <td>11.62</td>\n",
       "      <td>11.57</td>\n",
       "      <td>11.63</td>\n",
       "      <td>8.99</td>\n",
       "      <td>13.20</td>\n",
       "      <td>13.20</td>\n",
       "      <td>13.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 20:00:00</th>\n",
       "      <td>17.87</td>\n",
       "      <td>17.91</td>\n",
       "      <td>14.69</td>\n",
       "      <td>14.74</td>\n",
       "      <td>17.32</td>\n",
       "      <td>18.10</td>\n",
       "      <td>18.01</td>\n",
       "      <td>12.45</td>\n",
       "      <td>14.70</td>\n",
       "      <td>16.62</td>\n",
       "      <td>...</td>\n",
       "      <td>16.54</td>\n",
       "      <td>15.84</td>\n",
       "      <td>16.01</td>\n",
       "      <td>11.03</td>\n",
       "      <td>10.98</td>\n",
       "      <td>11.04</td>\n",
       "      <td>8.11</td>\n",
       "      <td>13.01</td>\n",
       "      <td>13.01</td>\n",
       "      <td>13.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 21:00:00</th>\n",
       "      <td>17.74</td>\n",
       "      <td>17.78</td>\n",
       "      <td>14.50</td>\n",
       "      <td>14.55</td>\n",
       "      <td>17.14</td>\n",
       "      <td>18.05</td>\n",
       "      <td>17.94</td>\n",
       "      <td>12.57</td>\n",
       "      <td>14.50</td>\n",
       "      <td>16.33</td>\n",
       "      <td>...</td>\n",
       "      <td>16.25</td>\n",
       "      <td>15.68</td>\n",
       "      <td>15.93</td>\n",
       "      <td>10.44</td>\n",
       "      <td>10.39</td>\n",
       "      <td>10.45</td>\n",
       "      <td>7.22</td>\n",
       "      <td>12.82</td>\n",
       "      <td>12.82</td>\n",
       "      <td>12.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 22:00:00</th>\n",
       "      <td>17.74</td>\n",
       "      <td>17.78</td>\n",
       "      <td>14.50</td>\n",
       "      <td>14.55</td>\n",
       "      <td>17.14</td>\n",
       "      <td>18.05</td>\n",
       "      <td>17.94</td>\n",
       "      <td>12.57</td>\n",
       "      <td>14.50</td>\n",
       "      <td>16.33</td>\n",
       "      <td>...</td>\n",
       "      <td>16.25</td>\n",
       "      <td>15.68</td>\n",
       "      <td>15.93</td>\n",
       "      <td>10.44</td>\n",
       "      <td>10.39</td>\n",
       "      <td>10.45</td>\n",
       "      <td>7.22</td>\n",
       "      <td>12.82</td>\n",
       "      <td>12.82</td>\n",
       "      <td>12.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:00:00</th>\n",
       "      <td>17.74</td>\n",
       "      <td>17.78</td>\n",
       "      <td>14.50</td>\n",
       "      <td>14.55</td>\n",
       "      <td>17.14</td>\n",
       "      <td>18.05</td>\n",
       "      <td>17.94</td>\n",
       "      <td>12.57</td>\n",
       "      <td>14.50</td>\n",
       "      <td>16.33</td>\n",
       "      <td>...</td>\n",
       "      <td>16.25</td>\n",
       "      <td>15.68</td>\n",
       "      <td>15.93</td>\n",
       "      <td>10.44</td>\n",
       "      <td>10.39</td>\n",
       "      <td>10.45</td>\n",
       "      <td>7.22</td>\n",
       "      <td>12.82</td>\n",
       "      <td>12.82</td>\n",
       "      <td>12.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 499 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       0      1      2      3      4      5      6      7    \\\n",
       "time                                                                          \n",
       "2019-01-01 00:00:00  10.31  10.35   1.65   1.70  11.53  10.76  10.57   0.43   \n",
       "2019-01-01 01:00:00  10.34  10.38   1.33   1.37  11.78  10.79  10.61   0.17   \n",
       "2019-01-01 02:00:00  10.36  10.40   1.01   1.05  12.03  10.82  10.64  -0.09   \n",
       "2019-01-01 03:00:00  10.39  10.43   0.69   0.73  12.28  10.84  10.68  -0.34   \n",
       "2019-01-01 04:00:00  10.27  10.31   0.47   0.51  12.13  10.73  10.57  -0.62   \n",
       "...                    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "2019-12-31 19:00:00  17.99  18.03  14.88  14.94  17.51  18.15  18.08  12.33   \n",
       "2019-12-31 20:00:00  17.87  17.91  14.69  14.74  17.32  18.10  18.01  12.45   \n",
       "2019-12-31 21:00:00  17.74  17.78  14.50  14.55  17.14  18.05  17.94  12.57   \n",
       "2019-12-31 22:00:00  17.74  17.78  14.50  14.55  17.14  18.05  17.94  12.57   \n",
       "2019-12-31 23:00:00  17.74  17.78  14.50  14.55  17.14  18.05  17.94  12.57   \n",
       "\n",
       "                       8      9    ...    489    490    491    492    493  \\\n",
       "time                               ...                                      \n",
       "2019-01-01 00:00:00   1.66  10.49  ...  10.40   9.53   9.94   3.19   3.14   \n",
       "2019-01-01 01:00:00   1.33  10.73  ...  10.65  10.21  10.63   3.70   3.65   \n",
       "2019-01-01 02:00:00   1.01  10.98  ...  10.90  10.88  11.33   4.22   4.17   \n",
       "2019-01-01 03:00:00   0.69  11.23  ...  11.14  11.56  12.02   4.74   4.69   \n",
       "2019-01-01 04:00:00   0.48  11.06  ...  10.97  11.33  11.79   4.47   4.42   \n",
       "...                    ...    ...  ...    ...    ...    ...    ...    ...   \n",
       "2019-12-31 19:00:00  14.89  16.91  ...  16.84  16.00  16.10  11.62  11.57   \n",
       "2019-12-31 20:00:00  14.70  16.62  ...  16.54  15.84  16.01  11.03  10.98   \n",
       "2019-12-31 21:00:00  14.50  16.33  ...  16.25  15.68  15.93  10.44  10.39   \n",
       "2019-12-31 22:00:00  14.50  16.33  ...  16.25  15.68  15.93  10.44  10.39   \n",
       "2019-12-31 23:00:00  14.50  16.33  ...  16.25  15.68  15.93  10.44  10.39   \n",
       "\n",
       "                       494   495    496    497    498  \n",
       "time                                                   \n",
       "2019-01-01 00:00:00   3.20  2.63   1.51   1.51   1.51  \n",
       "2019-01-01 01:00:00   3.71  2.08   1.21   1.21   1.21  \n",
       "2019-01-01 02:00:00   4.23  1.53   0.91   0.91   0.91  \n",
       "2019-01-01 03:00:00   4.75  0.99   0.60   0.60   0.60  \n",
       "2019-01-01 04:00:00   4.48  0.76   0.38   0.38   0.38  \n",
       "...                    ...   ...    ...    ...    ...  \n",
       "2019-12-31 19:00:00  11.63  8.99  13.20  13.20  13.20  \n",
       "2019-12-31 20:00:00  11.04  8.11  13.01  13.01  13.01  \n",
       "2019-12-31 21:00:00  10.45  7.22  12.82  12.82  12.82  \n",
       "2019-12-31 22:00:00  10.45  7.22  12.82  12.82  12.82  \n",
       "2019-12-31 23:00:00  10.45  7.22  12.82  12.82  12.82  \n",
       "\n",
       "[8760 rows x 499 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>489</th>\n",
       "      <th>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18.677202</td>\n",
       "      <td>18.695718</td>\n",
       "      <td>19.025963</td>\n",
       "      <td>19.074118</td>\n",
       "      <td>18.640517</td>\n",
       "      <td>18.575288</td>\n",
       "      <td>18.575945</td>\n",
       "      <td>16.410300</td>\n",
       "      <td>19.031903</td>\n",
       "      <td>18.483400</td>\n",
       "      <td>...</td>\n",
       "      <td>18.428421</td>\n",
       "      <td>18.076798</td>\n",
       "      <td>18.121936</td>\n",
       "      <td>18.029516</td>\n",
       "      <td>18.013313</td>\n",
       "      <td>18.032670</td>\n",
       "      <td>16.298357</td>\n",
       "      <td>17.600161</td>\n",
       "      <td>17.600161</td>\n",
       "      <td>17.600161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.170381</td>\n",
       "      <td>4.156910</td>\n",
       "      <td>8.816318</td>\n",
       "      <td>8.815930</td>\n",
       "      <td>4.049639</td>\n",
       "      <td>3.999922</td>\n",
       "      <td>4.043740</td>\n",
       "      <td>8.096028</td>\n",
       "      <td>8.816432</td>\n",
       "      <td>4.382668</td>\n",
       "      <td>...</td>\n",
       "      <td>4.401210</td>\n",
       "      <td>4.164785</td>\n",
       "      <td>4.071509</td>\n",
       "      <td>7.831887</td>\n",
       "      <td>7.862237</td>\n",
       "      <td>7.825749</td>\n",
       "      <td>8.248807</td>\n",
       "      <td>8.541373</td>\n",
       "      <td>8.541373</td>\n",
       "      <td>8.541373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.340000</td>\n",
       "      <td>8.380000</td>\n",
       "      <td>-1.220000</td>\n",
       "      <td>-1.180000</td>\n",
       "      <td>8.540000</td>\n",
       "      <td>8.550000</td>\n",
       "      <td>8.460000</td>\n",
       "      <td>-2.600000</td>\n",
       "      <td>-1.220000</td>\n",
       "      <td>7.560000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.460000</td>\n",
       "      <td>7.090000</td>\n",
       "      <td>7.390000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>1.680000</td>\n",
       "      <td>1.740000</td>\n",
       "      <td>-1.930000</td>\n",
       "      <td>-1.410000</td>\n",
       "      <td>-1.410000</td>\n",
       "      <td>-1.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15.477500</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>12.287500</td>\n",
       "      <td>12.337500</td>\n",
       "      <td>15.520000</td>\n",
       "      <td>15.520000</td>\n",
       "      <td>15.470000</td>\n",
       "      <td>10.097500</td>\n",
       "      <td>12.290000</td>\n",
       "      <td>15.050000</td>\n",
       "      <td>...</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>14.890000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>11.937500</td>\n",
       "      <td>11.890000</td>\n",
       "      <td>11.940000</td>\n",
       "      <td>9.917500</td>\n",
       "      <td>10.947500</td>\n",
       "      <td>10.947500</td>\n",
       "      <td>10.947500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.600000</td>\n",
       "      <td>18.630000</td>\n",
       "      <td>18.630000</td>\n",
       "      <td>18.680000</td>\n",
       "      <td>18.580000</td>\n",
       "      <td>18.525000</td>\n",
       "      <td>18.510000</td>\n",
       "      <td>15.830000</td>\n",
       "      <td>18.640000</td>\n",
       "      <td>18.380000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.335000</td>\n",
       "      <td>18.315000</td>\n",
       "      <td>18.330000</td>\n",
       "      <td>17.150000</td>\n",
       "      <td>17.125000</td>\n",
       "      <td>17.160000</td>\n",
       "      <td>15.570000</td>\n",
       "      <td>17.150000</td>\n",
       "      <td>17.150000</td>\n",
       "      <td>17.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>21.680000</td>\n",
       "      <td>21.692500</td>\n",
       "      <td>25.290000</td>\n",
       "      <td>25.330000</td>\n",
       "      <td>21.640000</td>\n",
       "      <td>21.470000</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>22.042500</td>\n",
       "      <td>25.292500</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>21.760000</td>\n",
       "      <td>21.380000</td>\n",
       "      <td>21.330000</td>\n",
       "      <td>23.270000</td>\n",
       "      <td>23.262500</td>\n",
       "      <td>23.262500</td>\n",
       "      <td>22.180000</td>\n",
       "      <td>23.762500</td>\n",
       "      <td>23.762500</td>\n",
       "      <td>23.762500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.440000</td>\n",
       "      <td>31.420000</td>\n",
       "      <td>42.070000</td>\n",
       "      <td>42.120000</td>\n",
       "      <td>29.450000</td>\n",
       "      <td>30.900000</td>\n",
       "      <td>31.010000</td>\n",
       "      <td>38.570000</td>\n",
       "      <td>42.080000</td>\n",
       "      <td>29.650000</td>\n",
       "      <td>...</td>\n",
       "      <td>29.650000</td>\n",
       "      <td>29.680000</td>\n",
       "      <td>29.840000</td>\n",
       "      <td>43.050000</td>\n",
       "      <td>43.100000</td>\n",
       "      <td>43.040000</td>\n",
       "      <td>39.010000</td>\n",
       "      <td>39.710000</td>\n",
       "      <td>39.710000</td>\n",
       "      <td>39.710000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 499 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0            1            2            3            4    \\\n",
       "count  8760.000000  8760.000000  8760.000000  8760.000000  8760.000000   \n",
       "mean     18.677202    18.695718    19.025963    19.074118    18.640517   \n",
       "std       4.170381     4.156910     8.816318     8.815930     4.049639   \n",
       "min       8.340000     8.380000    -1.220000    -1.180000     8.540000   \n",
       "25%      15.477500    15.500000    12.287500    12.337500    15.520000   \n",
       "50%      18.600000    18.630000    18.630000    18.680000    18.580000   \n",
       "75%      21.680000    21.692500    25.290000    25.330000    21.640000   \n",
       "max      31.440000    31.420000    42.070000    42.120000    29.450000   \n",
       "\n",
       "               5            6            7            8            9    ...  \\\n",
       "count  8760.000000  8760.000000  8760.000000  8760.000000  8760.000000  ...   \n",
       "mean     18.575288    18.575945    16.410300    19.031903    18.483400  ...   \n",
       "std       3.999922     4.043740     8.096028     8.816432     4.382668  ...   \n",
       "min       8.550000     8.460000    -2.600000    -1.220000     7.560000  ...   \n",
       "25%      15.520000    15.470000    10.097500    12.290000    15.050000  ...   \n",
       "50%      18.525000    18.510000    15.830000    18.640000    18.380000  ...   \n",
       "75%      21.470000    21.500000    22.042500    25.292500    21.800000  ...   \n",
       "max      30.900000    31.010000    38.570000    42.080000    29.650000  ...   \n",
       "\n",
       "               489          490          491          492          493  \\\n",
       "count  8760.000000  8760.000000  8760.000000  8760.000000  8760.000000   \n",
       "mean     18.428421    18.076798    18.121936    18.029516    18.013313   \n",
       "std       4.401210     4.164785     4.071509     7.831887     7.862237   \n",
       "min       7.460000     7.090000     7.390000     1.730000     1.680000   \n",
       "25%      14.970000    14.890000    15.000000    11.937500    11.890000   \n",
       "50%      18.335000    18.315000    18.330000    17.150000    17.125000   \n",
       "75%      21.760000    21.380000    21.330000    23.270000    23.262500   \n",
       "max      29.650000    29.680000    29.840000    43.050000    43.100000   \n",
       "\n",
       "               494          495          496          497          498  \n",
       "count  8760.000000  8760.000000  8760.000000  8760.000000  8760.000000  \n",
       "mean     18.032670    16.298357    17.600161    17.600161    17.600161  \n",
       "std       7.825749     8.248807     8.541373     8.541373     8.541373  \n",
       "min       1.740000    -1.930000    -1.410000    -1.410000    -1.410000  \n",
       "25%      11.940000     9.917500    10.947500    10.947500    10.947500  \n",
       "50%      17.160000    15.570000    17.150000    17.150000    17.150000  \n",
       "75%      23.262500    22.180000    23.762500    23.762500    23.762500  \n",
       "max      43.040000    39.010000    39.710000    39.710000    39.710000  \n",
       "\n",
       "[8 rows x 499 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example Plot for Weather Data (Outside Temperature) of Customer #20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "customer = 20\n",
    "_ = weather.loc[:, customer].plot(color='orange', alpha=0.7, ylabel='outside temperature in °C',\n",
    "                                 title='Weather (Outside Temperature) of Customer #' + str(customer))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Customer Profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers = pd.read_excel(files[categories.index('profiles')])\n",
    "customers.columns = ['customer', 'profile']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *customers* dataframe contains **499** rows. A row represents a single *customer* with a customer ID in the range of *0..498*. For each customer an anonymized identifier as well as a *profile* are stored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer</th>\n",
       "      <th>profile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5d6fcd1cf44b0324bc6b7254</td>\n",
       "      <td>Actividades de los hogares como productores de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5d6fcd1cf44b0324bc6b7257</td>\n",
       "      <td>Actividades de los hogares como productores de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5d6fcd1cf44b0324bc6b725a</td>\n",
       "      <td>Comercio al por menor de frutas y hortalizas e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5d6fcd1cf44b0324bc6b725d</td>\n",
       "      <td>Actividades de los hogares como productores de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5d6fcd1df44b0324bc6b7260</td>\n",
       "      <td>Actividades de los hogares como productores de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>5d6fcd7df44b0324bc6b79f7</td>\n",
       "      <td>Actividades de los hogares como productores de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>5d6fcd7df44b0324bc6b79fd</td>\n",
       "      <td>Actividades de los hogares como productores de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>5d6fcd7ef44b0324bc6b7a00</td>\n",
       "      <td>Otras actividades asociativas n.c.o.p.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>5d6fcd7ef44b0324bc6b7a02</td>\n",
       "      <td>Otras actividades asociativas n.c.o.p.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>5d6fcd7ef44b0324bc6b7a03</td>\n",
       "      <td>Otras actividades asociativas n.c.o.p.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>499 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     customer  \\\n",
       "0    5d6fcd1cf44b0324bc6b7254   \n",
       "1    5d6fcd1cf44b0324bc6b7257   \n",
       "2    5d6fcd1cf44b0324bc6b725a   \n",
       "3    5d6fcd1cf44b0324bc6b725d   \n",
       "4    5d6fcd1df44b0324bc6b7260   \n",
       "..                        ...   \n",
       "494  5d6fcd7df44b0324bc6b79f7   \n",
       "495  5d6fcd7df44b0324bc6b79fd   \n",
       "496  5d6fcd7ef44b0324bc6b7a00   \n",
       "497  5d6fcd7ef44b0324bc6b7a02   \n",
       "498  5d6fcd7ef44b0324bc6b7a03   \n",
       "\n",
       "                                               profile  \n",
       "0    Actividades de los hogares como productores de...  \n",
       "1    Actividades de los hogares como productores de...  \n",
       "2    Comercio al por menor de frutas y hortalizas e...  \n",
       "3    Actividades de los hogares como productores de...  \n",
       "4    Actividades de los hogares como productores de...  \n",
       "..                                                 ...  \n",
       "494  Actividades de los hogares como productores de...  \n",
       "495  Actividades de los hogares como productores de...  \n",
       "496             Otras actividades asociativas n.c.o.p.  \n",
       "497             Otras actividades asociativas n.c.o.p.  \n",
       "498             Otras actividades asociativas n.c.o.p.  \n",
       "\n",
       "[499 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting Profiles "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "profiles = pd.DataFrame(customers['profile'].unique(), columns=['profile'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are **68** different customer profiles. The dataframe *profiles* contains all these profile descriptions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>profile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Actividades de los hogares como productores de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Comercio al por menor de frutas y hortalizas e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fabricación de galletas y productos de panader...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Actividades de contabilidad, teneduría de libr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Restaurantes y puestos de comidas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Actividades generales de la Administración Púb...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Inversión colectiva, fondos y entidades financ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Construcción de edificios</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Actividades de fotocopiado, preparación de doc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Compraventa de bienes inmobiliarios por cuenta...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              profile\n",
       "0   Actividades de los hogares como productores de...\n",
       "1   Comercio al por menor de frutas y hortalizas e...\n",
       "2   Fabricación de galletas y productos de panader...\n",
       "3   Actividades de contabilidad, teneduría de libr...\n",
       "4                   Restaurantes y puestos de comidas\n",
       "..                                                ...\n",
       "63  Actividades generales de la Administración Púb...\n",
       "64  Inversión colectiva, fondos y entidades financ...\n",
       "65                          Construcción de edificios\n",
       "66  Actividades de fotocopiado, preparación de doc...\n",
       "67  Compraventa de bienes inmobiliarios por cuenta...\n",
       "\n",
       "[68 rows x 1 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating Synthetic Weather Forecast Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_forecast = weather.copy()\n",
    "weather_forecast.index = weather_forecast.index + pd.Timedelta(days=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As there were no weather forecast data available, the forecast has been calculated synthetically. The dataframe *weather_forecast* contains **499** columns for all customers. Each row in the dataframe contains the synthetically predicted outside temperature for the day ahead. Since the weather time series were recorded with an hourly resolution, the dataframe contains 8760 rows (365 day x 24 hours, Jan 02, 2019 - Jan 01, 2020)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>1</th>\n",
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       "      <th>3</th>\n",
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       "      <th>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
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       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-02 00:00:00</th>\n",
       "      <td>10.31</td>\n",
       "      <td>10.35</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.70</td>\n",
       "      <td>11.53</td>\n",
       "      <td>10.76</td>\n",
       "      <td>10.57</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.66</td>\n",
       "      <td>10.49</td>\n",
       "      <td>...</td>\n",
       "      <td>10.40</td>\n",
       "      <td>9.53</td>\n",
       "      <td>9.94</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.14</td>\n",
       "      <td>3.20</td>\n",
       "      <td>2.63</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02 01:00:00</th>\n",
       "      <td>10.34</td>\n",
       "      <td>10.38</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.37</td>\n",
       "      <td>11.78</td>\n",
       "      <td>10.79</td>\n",
       "      <td>10.61</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.33</td>\n",
       "      <td>10.73</td>\n",
       "      <td>...</td>\n",
       "      <td>10.65</td>\n",
       "      <td>10.21</td>\n",
       "      <td>10.63</td>\n",
       "      <td>3.70</td>\n",
       "      <td>3.65</td>\n",
       "      <td>3.71</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02 02:00:00</th>\n",
       "      <td>10.36</td>\n",
       "      <td>10.40</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.05</td>\n",
       "      <td>12.03</td>\n",
       "      <td>10.82</td>\n",
       "      <td>10.64</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>1.01</td>\n",
       "      <td>10.98</td>\n",
       "      <td>...</td>\n",
       "      <td>10.90</td>\n",
       "      <td>10.88</td>\n",
       "      <td>11.33</td>\n",
       "      <td>4.22</td>\n",
       "      <td>4.17</td>\n",
       "      <td>4.23</td>\n",
       "      <td>1.53</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02 03:00:00</th>\n",
       "      <td>10.39</td>\n",
       "      <td>10.43</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.73</td>\n",
       "      <td>12.28</td>\n",
       "      <td>10.84</td>\n",
       "      <td>10.68</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>0.69</td>\n",
       "      <td>11.23</td>\n",
       "      <td>...</td>\n",
       "      <td>11.14</td>\n",
       "      <td>11.56</td>\n",
       "      <td>12.02</td>\n",
       "      <td>4.74</td>\n",
       "      <td>4.69</td>\n",
       "      <td>4.75</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02 04:00:00</th>\n",
       "      <td>10.27</td>\n",
       "      <td>10.31</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.51</td>\n",
       "      <td>12.13</td>\n",
       "      <td>10.73</td>\n",
       "      <td>10.57</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>0.48</td>\n",
       "      <td>11.06</td>\n",
       "      <td>...</td>\n",
       "      <td>10.97</td>\n",
       "      <td>11.33</td>\n",
       "      <td>11.79</td>\n",
       "      <td>4.47</td>\n",
       "      <td>4.42</td>\n",
       "      <td>4.48</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 19:00:00</th>\n",
       "      <td>17.99</td>\n",
       "      <td>18.03</td>\n",
       "      <td>14.88</td>\n",
       "      <td>14.94</td>\n",
       "      <td>17.51</td>\n",
       "      <td>18.15</td>\n",
       "      <td>18.08</td>\n",
       "      <td>12.33</td>\n",
       "      <td>14.89</td>\n",
       "      <td>16.91</td>\n",
       "      <td>...</td>\n",
       "      <td>16.84</td>\n",
       "      <td>16.00</td>\n",
       "      <td>16.10</td>\n",
       "      <td>11.62</td>\n",
       "      <td>11.57</td>\n",
       "      <td>11.63</td>\n",
       "      <td>8.99</td>\n",
       "      <td>13.20</td>\n",
       "      <td>13.20</td>\n",
       "      <td>13.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 20:00:00</th>\n",
       "      <td>17.87</td>\n",
       "      <td>17.91</td>\n",
       "      <td>14.69</td>\n",
       "      <td>14.74</td>\n",
       "      <td>17.32</td>\n",
       "      <td>18.10</td>\n",
       "      <td>18.01</td>\n",
       "      <td>12.45</td>\n",
       "      <td>14.70</td>\n",
       "      <td>16.62</td>\n",
       "      <td>...</td>\n",
       "      <td>16.54</td>\n",
       "      <td>15.84</td>\n",
       "      <td>16.01</td>\n",
       "      <td>11.03</td>\n",
       "      <td>10.98</td>\n",
       "      <td>11.04</td>\n",
       "      <td>8.11</td>\n",
       "      <td>13.01</td>\n",
       "      <td>13.01</td>\n",
       "      <td>13.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 21:00:00</th>\n",
       "      <td>17.74</td>\n",
       "      <td>17.78</td>\n",
       "      <td>14.50</td>\n",
       "      <td>14.55</td>\n",
       "      <td>17.14</td>\n",
       "      <td>18.05</td>\n",
       "      <td>17.94</td>\n",
       "      <td>12.57</td>\n",
       "      <td>14.50</td>\n",
       "      <td>16.33</td>\n",
       "      <td>...</td>\n",
       "      <td>16.25</td>\n",
       "      <td>15.68</td>\n",
       "      <td>15.93</td>\n",
       "      <td>10.44</td>\n",
       "      <td>10.39</td>\n",
       "      <td>10.45</td>\n",
       "      <td>7.22</td>\n",
       "      <td>12.82</td>\n",
       "      <td>12.82</td>\n",
       "      <td>12.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 22:00:00</th>\n",
       "      <td>17.74</td>\n",
       "      <td>17.78</td>\n",
       "      <td>14.50</td>\n",
       "      <td>14.55</td>\n",
       "      <td>17.14</td>\n",
       "      <td>18.05</td>\n",
       "      <td>17.94</td>\n",
       "      <td>12.57</td>\n",
       "      <td>14.50</td>\n",
       "      <td>16.33</td>\n",
       "      <td>...</td>\n",
       "      <td>16.25</td>\n",
       "      <td>15.68</td>\n",
       "      <td>15.93</td>\n",
       "      <td>10.44</td>\n",
       "      <td>10.39</td>\n",
       "      <td>10.45</td>\n",
       "      <td>7.22</td>\n",
       "      <td>12.82</td>\n",
       "      <td>12.82</td>\n",
       "      <td>12.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 23:00:00</th>\n",
       "      <td>17.74</td>\n",
       "      <td>17.78</td>\n",
       "      <td>14.50</td>\n",
       "      <td>14.55</td>\n",
       "      <td>17.14</td>\n",
       "      <td>18.05</td>\n",
       "      <td>17.94</td>\n",
       "      <td>12.57</td>\n",
       "      <td>14.50</td>\n",
       "      <td>16.33</td>\n",
       "      <td>...</td>\n",
       "      <td>16.25</td>\n",
       "      <td>15.68</td>\n",
       "      <td>15.93</td>\n",
       "      <td>10.44</td>\n",
       "      <td>10.39</td>\n",
       "      <td>10.45</td>\n",
       "      <td>7.22</td>\n",
       "      <td>12.82</td>\n",
       "      <td>12.82</td>\n",
       "      <td>12.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 499 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       0      1      2      3      4      5      6      7    \\\n",
       "time                                                                          \n",
       "2019-01-02 00:00:00  10.31  10.35   1.65   1.70  11.53  10.76  10.57   0.43   \n",
       "2019-01-02 01:00:00  10.34  10.38   1.33   1.37  11.78  10.79  10.61   0.17   \n",
       "2019-01-02 02:00:00  10.36  10.40   1.01   1.05  12.03  10.82  10.64  -0.09   \n",
       "2019-01-02 03:00:00  10.39  10.43   0.69   0.73  12.28  10.84  10.68  -0.34   \n",
       "2019-01-02 04:00:00  10.27  10.31   0.47   0.51  12.13  10.73  10.57  -0.62   \n",
       "...                    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "2020-01-01 19:00:00  17.99  18.03  14.88  14.94  17.51  18.15  18.08  12.33   \n",
       "2020-01-01 20:00:00  17.87  17.91  14.69  14.74  17.32  18.10  18.01  12.45   \n",
       "2020-01-01 21:00:00  17.74  17.78  14.50  14.55  17.14  18.05  17.94  12.57   \n",
       "2020-01-01 22:00:00  17.74  17.78  14.50  14.55  17.14  18.05  17.94  12.57   \n",
       "2020-01-01 23:00:00  17.74  17.78  14.50  14.55  17.14  18.05  17.94  12.57   \n",
       "\n",
       "                       8      9    ...    489    490    491    492    493  \\\n",
       "time                               ...                                      \n",
       "2019-01-02 00:00:00   1.66  10.49  ...  10.40   9.53   9.94   3.19   3.14   \n",
       "2019-01-02 01:00:00   1.33  10.73  ...  10.65  10.21  10.63   3.70   3.65   \n",
       "2019-01-02 02:00:00   1.01  10.98  ...  10.90  10.88  11.33   4.22   4.17   \n",
       "2019-01-02 03:00:00   0.69  11.23  ...  11.14  11.56  12.02   4.74   4.69   \n",
       "2019-01-02 04:00:00   0.48  11.06  ...  10.97  11.33  11.79   4.47   4.42   \n",
       "...                    ...    ...  ...    ...    ...    ...    ...    ...   \n",
       "2020-01-01 19:00:00  14.89  16.91  ...  16.84  16.00  16.10  11.62  11.57   \n",
       "2020-01-01 20:00:00  14.70  16.62  ...  16.54  15.84  16.01  11.03  10.98   \n",
       "2020-01-01 21:00:00  14.50  16.33  ...  16.25  15.68  15.93  10.44  10.39   \n",
       "2020-01-01 22:00:00  14.50  16.33  ...  16.25  15.68  15.93  10.44  10.39   \n",
       "2020-01-01 23:00:00  14.50  16.33  ...  16.25  15.68  15.93  10.44  10.39   \n",
       "\n",
       "                       494   495    496    497    498  \n",
       "time                                                   \n",
       "2019-01-02 00:00:00   3.20  2.63   1.51   1.51   1.51  \n",
       "2019-01-02 01:00:00   3.71  2.08   1.21   1.21   1.21  \n",
       "2019-01-02 02:00:00   4.23  1.53   0.91   0.91   0.91  \n",
       "2019-01-02 03:00:00   4.75  0.99   0.60   0.60   0.60  \n",
       "2019-01-02 04:00:00   4.48  0.76   0.38   0.38   0.38  \n",
       "...                    ...   ...    ...    ...    ...  \n",
       "2020-01-01 19:00:00  11.63  8.99  13.20  13.20  13.20  \n",
       "2020-01-01 20:00:00  11.04  8.11  13.01  13.01  13.01  \n",
       "2020-01-01 21:00:00  10.45  7.22  12.82  12.82  12.82  \n",
       "2020-01-01 22:00:00  10.45  7.22  12.82  12.82  12.82  \n",
       "2020-01-01 23:00:00  10.45  7.22  12.82  12.82  12.82  \n",
       "\n",
       "[8760 rows x 499 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_forecast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Holidays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "holidays = hd.ES(years=list(range(2010, 2021)), prov=\"MD\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the energy consumption behavior depends on weekly seasonalities like *working day* or public *holiday*, a list of all public holidays is needed for feature extraction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{datetime.date(2016, 1, 1): 'Año nuevo',\n",
       " datetime.date(2016, 1, 6): 'Epifanía del Señor',\n",
       " datetime.date(2016, 3, 24): 'Jueves Santo',\n",
       " datetime.date(2016, 3, 25): 'Viernes Santo',\n",
       " datetime.date(2016, 5, 2): 'Día de Comunidad de Madrid, Día del Trabajador (Trasladado)',\n",
       " datetime.date(2016, 8, 15): 'Asunción de la Virgen',\n",
       " datetime.date(2016, 10, 12): 'Día de la Hispanidad',\n",
       " datetime.date(2016, 11, 1): 'Todos los Santos',\n",
       " datetime.date(2016, 12, 6): 'Día de la Constitución Española',\n",
       " datetime.date(2016, 12, 8): 'La Inmaculada Concepción',\n",
       " datetime.date(2016, 12, 26): 'Navidad (Trasladado)',\n",
       " datetime.date(2017, 1, 2): 'Año nuevo (Trasladado)',\n",
       " datetime.date(2017, 1, 6): 'Epifanía del Señor',\n",
       " datetime.date(2017, 4, 13): 'Jueves Santo',\n",
       " datetime.date(2017, 4, 14): 'Viernes Santo',\n",
       " datetime.date(2017, 5, 1): 'Día del Trabajador',\n",
       " datetime.date(2017, 8, 15): 'Asunción de la Virgen',\n",
       " datetime.date(2017, 10, 12): 'Día de la Hispanidad',\n",
       " datetime.date(2017, 11, 1): 'Todos los Santos',\n",
       " datetime.date(2017, 12, 6): 'Día de la Constitución Española',\n",
       " datetime.date(2017, 12, 8): 'La Inmaculada Concepción',\n",
       " datetime.date(2017, 12, 25): 'Navidad',\n",
       " datetime.date(2017, 5, 2): 'Día de Comunidad de Madrid',\n",
       " datetime.date(2018, 1, 1): 'Año nuevo',\n",
       " datetime.date(2018, 1, 6): 'Epifanía del Señor',\n",
       " datetime.date(2018, 3, 29): 'Jueves Santo',\n",
       " datetime.date(2018, 3, 30): 'Viernes Santo',\n",
       " datetime.date(2018, 5, 1): 'Día del Trabajador',\n",
       " datetime.date(2018, 8, 15): 'Asunción de la Virgen',\n",
       " datetime.date(2018, 10, 12): 'Día de la Hispanidad',\n",
       " datetime.date(2018, 11, 1): 'Todos los Santos',\n",
       " datetime.date(2018, 12, 6): 'Día de la Constitución Española',\n",
       " datetime.date(2018, 12, 8): 'La Inmaculada Concepción',\n",
       " datetime.date(2018, 12, 25): 'Navidad',\n",
       " datetime.date(2018, 5, 2): 'Día de Comunidad de Madrid',\n",
       " datetime.date(2019, 1, 1): 'Año nuevo',\n",
       " datetime.date(2019, 1, 7): 'Epifanía del Señor (Trasladado)',\n",
       " datetime.date(2019, 4, 18): 'Jueves Santo',\n",
       " datetime.date(2019, 4, 19): 'Viernes Santo',\n",
       " datetime.date(2019, 5, 1): 'Día del Trabajador',\n",
       " datetime.date(2019, 8, 15): 'Asunción de la Virgen',\n",
       " datetime.date(2019, 10, 12): 'Día de la Hispanidad',\n",
       " datetime.date(2019, 11, 1): 'Todos los Santos',\n",
       " datetime.date(2019, 12, 6): 'Día de la Constitución Española',\n",
       " datetime.date(2019, 12, 9): 'La Inmaculada Concepción (Trasladado)',\n",
       " datetime.date(2019, 12, 25): 'Navidad',\n",
       " datetime.date(2019, 5, 2): 'Día de Comunidad de Madrid',\n",
       " datetime.date(2020, 1, 1): 'Año nuevo',\n",
       " datetime.date(2020, 1, 6): 'Epifanía del Señor',\n",
       " datetime.date(2020, 4, 9): 'Jueves Santo',\n",
       " datetime.date(2020, 4, 10): 'Viernes Santo',\n",
       " datetime.date(2020, 5, 1): 'Día del Trabajador',\n",
       " datetime.date(2020, 8, 15): 'Asunción de la Virgen',\n",
       " datetime.date(2020, 10, 12): 'Día de la Hispanidad',\n",
       " datetime.date(2020, 11, 2): 'Todos los Santos (Trasladado)',\n",
       " datetime.date(2020, 12, 7): 'Día de la Constitución Española (Trasladado)',\n",
       " datetime.date(2020, 12, 8): 'La Inmaculada Concepción',\n",
       " datetime.date(2020, 12, 25): 'Navidad',\n",
       " datetime.date(2020, 5, 2): 'Día de Comunidad de Madrid',\n",
       " datetime.date(2010, 1, 1): 'Año nuevo',\n",
       " datetime.date(2010, 1, 6): 'Epifanía del Señor',\n",
       " datetime.date(2010, 3, 19): 'San José',\n",
       " datetime.date(2010, 4, 1): 'Jueves Santo',\n",
       " datetime.date(2010, 4, 2): 'Viernes Santo',\n",
       " datetime.date(2010, 5, 1): 'Día del Trabajador',\n",
       " datetime.date(2010, 8, 16): 'Asunción de la Virgen (Trasladado)',\n",
       " datetime.date(2010, 10, 12): 'Día de la Hispanidad',\n",
       " datetime.date(2010, 11, 1): 'Todos los Santos',\n",
       " datetime.date(2010, 12, 6): 'Día de la Constitución Española',\n",
       " datetime.date(2010, 12, 8): 'La Inmaculada Concepción',\n",
       " datetime.date(2010, 12, 25): 'Navidad',\n",
       " datetime.date(2010, 5, 3): 'Día de Comunidad de Madrid (Trasladado)',\n",
       " datetime.date(2011, 1, 1): 'Año nuevo',\n",
       " datetime.date(2011, 1, 6): 'Epifanía del Señor',\n",
       " datetime.date(2011, 3, 19): 'San José',\n",
       " datetime.date(2011, 4, 21): 'Jueves Santo',\n",
       " datetime.date(2011, 4, 22): 'Viernes Santo',\n",
       " datetime.date(2011, 5, 2): 'Día de Comunidad de Madrid, Día del Trabajador (Trasladado)',\n",
       " datetime.date(2011, 8, 15): 'Asunción de la Virgen',\n",
       " datetime.date(2011, 10, 12): 'Día de la Hispanidad',\n",
       " datetime.date(2011, 11, 1): 'Todos los Santos',\n",
       " datetime.date(2011, 12, 6): 'Día de la Constitución Española',\n",
       " datetime.date(2011, 12, 8): 'La Inmaculada Concepción',\n",
       " datetime.date(2011, 12, 26): 'Navidad (Trasladado)',\n",
       " datetime.date(2012, 1, 2): 'Año nuevo (Trasladado)',\n",
       " datetime.date(2012, 1, 6): 'Epifanía del Señor',\n",
       " datetime.date(2012, 3, 19): 'San José',\n",
       " datetime.date(2012, 4, 5): 'Jueves Santo',\n",
       " datetime.date(2012, 4, 6): 'Viernes Santo',\n",
       " datetime.date(2012, 5, 1): 'Día del Trabajador',\n",
       " datetime.date(2012, 8, 15): 'Asunción de la Virgen',\n",
       " datetime.date(2012, 10, 12): 'Día de la Hispanidad',\n",
       " datetime.date(2012, 11, 1): 'Todos los Santos',\n",
       " datetime.date(2012, 12, 6): 'Día de la Constitución Española',\n",
       " datetime.date(2012, 12, 8): 'La Inmaculada Concepción',\n",
       " datetime.date(2012, 12, 25): 'Navidad',\n",
       " datetime.date(2012, 5, 2): 'Día de Comunidad de Madrid',\n",
       " datetime.date(2013, 1, 1): 'Año nuevo',\n",
       " datetime.date(2013, 1, 7): 'Epifanía del Señor (Trasladado)',\n",
       " datetime.date(2013, 3, 19): 'San José',\n",
       " datetime.date(2013, 3, 28): 'Jueves Santo',\n",
       " datetime.date(2013, 3, 29): 'Viernes Santo',\n",
       " datetime.date(2013, 5, 1): 'Día del Trabajador',\n",
       " datetime.date(2013, 8, 15): 'Asunción de la Virgen',\n",
       " datetime.date(2013, 10, 12): 'Día de la Hispanidad',\n",
       " datetime.date(2013, 11, 1): 'Todos los Santos',\n",
       " datetime.date(2013, 12, 6): 'Día de la Constitución Española',\n",
       " datetime.date(2013, 12, 9): 'La Inmaculada Concepción (Trasladado)',\n",
       " datetime.date(2013, 12, 25): 'Navidad',\n",
       " datetime.date(2013, 5, 2): 'Día de Comunidad de Madrid',\n",
       " datetime.date(2014, 1, 1): 'Año nuevo',\n",
       " datetime.date(2014, 1, 6): 'Epifanía del Señor',\n",
       " datetime.date(2014, 3, 19): 'San José',\n",
       " datetime.date(2014, 4, 17): 'Jueves Santo',\n",
       " datetime.date(2014, 4, 18): 'Viernes Santo',\n",
       " datetime.date(2014, 5, 1): 'Día del Trabajador',\n",
       " datetime.date(2014, 8, 15): 'Asunción de la Virgen',\n",
       " datetime.date(2014, 10, 13): 'Día de la Hispanidad (Trasladado)',\n",
       " datetime.date(2014, 11, 1): 'Todos los Santos',\n",
       " datetime.date(2014, 12, 6): 'Día de la Constitución Española',\n",
       " datetime.date(2014, 12, 8): 'La Inmaculada Concepción',\n",
       " datetime.date(2014, 12, 25): 'Navidad',\n",
       " datetime.date(2014, 5, 2): 'Día de Comunidad de Madrid',\n",
       " datetime.date(2015, 1, 1): 'Año nuevo',\n",
       " datetime.date(2015, 1, 6): 'Epifanía del Señor',\n",
       " datetime.date(2015, 3, 19): 'San José',\n",
       " datetime.date(2015, 4, 2): 'Jueves Santo',\n",
       " datetime.date(2015, 4, 3): 'Viernes Santo',\n",
       " datetime.date(2015, 5, 1): 'Día del Trabajador',\n",
       " datetime.date(2015, 8, 15): 'Asunción de la Virgen',\n",
       " datetime.date(2015, 10, 12): 'Día de la Hispanidad',\n",
       " datetime.date(2015, 11, 2): 'Todos los Santos (Trasladado)',\n",
       " datetime.date(2015, 12, 7): 'Día de la Constitución Española (Trasladado)',\n",
       " datetime.date(2015, 12, 8): 'La Inmaculada Concepción',\n",
       " datetime.date(2015, 12, 25): 'Navidad',\n",
       " datetime.date(2015, 5, 2): 'Día de Comunidad de Madrid'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holidays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating Day Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "days = pd.DataFrame(pd.to_datetime(consumptions.index.date), index=consumptions.index, columns=['date'])\n",
    "days['day_of_week'] = list(days.index.dayofweek)\n",
    "days['day_of_month'] = list(days.index.day)\n",
    "days['month'] = list(days.index.month)\n",
    "days['day_category'] = days['day_of_week'].replace({0:0,1:1,2:1,3:1,4:2,5:3,6:4})\n",
    "days.loc[days['date'].apply(lambda d: d in holidays), 'day_category'] = 4\n",
    "days = days.groupby(['date']).first()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataframe *days* contains additional information for characterizing a certain date (day). For each day within the measurement period the day of the week, the day within the month, the month as well as the day category was calculated. The used day categories are *'Monday'* (0), *'Tuesday-Thursday'* (1), *'Friday'* (2), *'Saturday'* (3), and *'Sunday or Holiday'* (4)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>month</th>\n",
       "      <th>day_category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-27</th>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-28</th>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29</th>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30</th>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            day_of_week  day_of_month  month  day_category\n",
       "date                                                      \n",
       "2019-01-01            1             1      1             4\n",
       "2019-01-02            2             2      1             1\n",
       "2019-01-03            3             3      1             1\n",
       "2019-01-04            4             4      1             2\n",
       "2019-01-05            5             5      1             3\n",
       "...                 ...           ...    ...           ...\n",
       "2019-12-27            4            27     12             2\n",
       "2019-12-28            5            28     12             3\n",
       "2019-12-29            6            29     12             4\n",
       "2019-12-30            0            30     12             0\n",
       "2019-12-31            1            31     12             1\n",
       "\n",
       "[365 rows x 4 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating the Daily Mean for the Consumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "consumptions_daily_mean = pd.DataFrame(consumptions.groupby(consumptions.index.date).mean(), index=days.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataframe *consumptions_daily_mean* contains the daily mean values for the energy consumption of all customers. This means the table has **499** columns (customers) and **365** rows (days within the measurement period)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>489</th>\n",
       "      <th>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>0.107208</td>\n",
       "      <td>0.119875</td>\n",
       "      <td>0.932625</td>\n",
       "      <td>0.432833</td>\n",
       "      <td>0.580750</td>\n",
       "      <td>0.155292</td>\n",
       "      <td>3.258292</td>\n",
       "      <td>3.083333</td>\n",
       "      <td>0.310667</td>\n",
       "      <td>0.663500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.484083</td>\n",
       "      <td>0.090833</td>\n",
       "      <td>0.017417</td>\n",
       "      <td>0.257833</td>\n",
       "      <td>0.008958</td>\n",
       "      <td>0.876458</td>\n",
       "      <td>0.107708</td>\n",
       "      <td>0.249500</td>\n",
       "      <td>0.280333</td>\n",
       "      <td>0.341458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>0.145333</td>\n",
       "      <td>0.112542</td>\n",
       "      <td>1.224708</td>\n",
       "      <td>0.394375</td>\n",
       "      <td>0.312583</td>\n",
       "      <td>0.201458</td>\n",
       "      <td>3.240833</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>0.359667</td>\n",
       "      <td>1.056167</td>\n",
       "      <td>...</td>\n",
       "      <td>0.535167</td>\n",
       "      <td>0.207208</td>\n",
       "      <td>0.017833</td>\n",
       "      <td>1.256625</td>\n",
       "      <td>0.008792</td>\n",
       "      <td>1.031042</td>\n",
       "      <td>0.043375</td>\n",
       "      <td>0.256208</td>\n",
       "      <td>0.329125</td>\n",
       "      <td>0.364458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>0.453875</td>\n",
       "      <td>0.297750</td>\n",
       "      <td>1.159792</td>\n",
       "      <td>0.532375</td>\n",
       "      <td>0.418292</td>\n",
       "      <td>0.300583</td>\n",
       "      <td>3.323167</td>\n",
       "      <td>2.791667</td>\n",
       "      <td>0.331083</td>\n",
       "      <td>0.842958</td>\n",
       "      <td>...</td>\n",
       "      <td>0.588833</td>\n",
       "      <td>0.226792</td>\n",
       "      <td>0.018042</td>\n",
       "      <td>1.335750</td>\n",
       "      <td>0.008917</td>\n",
       "      <td>1.105083</td>\n",
       "      <td>0.041417</td>\n",
       "      <td>0.277292</td>\n",
       "      <td>0.325250</td>\n",
       "      <td>0.367042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>0.640625</td>\n",
       "      <td>0.288208</td>\n",
       "      <td>1.158750</td>\n",
       "      <td>0.387958</td>\n",
       "      <td>0.459500</td>\n",
       "      <td>0.205417</td>\n",
       "      <td>3.309583</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>0.280542</td>\n",
       "      <td>1.194917</td>\n",
       "      <td>...</td>\n",
       "      <td>0.486542</td>\n",
       "      <td>0.215167</td>\n",
       "      <td>0.017917</td>\n",
       "      <td>1.038750</td>\n",
       "      <td>0.008875</td>\n",
       "      <td>1.100333</td>\n",
       "      <td>0.038833</td>\n",
       "      <td>0.317458</td>\n",
       "      <td>0.329500</td>\n",
       "      <td>0.371917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>0.482958</td>\n",
       "      <td>0.285708</td>\n",
       "      <td>1.099417</td>\n",
       "      <td>0.519792</td>\n",
       "      <td>0.191208</td>\n",
       "      <td>0.213208</td>\n",
       "      <td>3.298625</td>\n",
       "      <td>2.833333</td>\n",
       "      <td>0.221875</td>\n",
       "      <td>0.951708</td>\n",
       "      <td>...</td>\n",
       "      <td>0.376167</td>\n",
       "      <td>0.086333</td>\n",
       "      <td>0.017625</td>\n",
       "      <td>0.543000</td>\n",
       "      <td>0.363250</td>\n",
       "      <td>1.001625</td>\n",
       "      <td>0.038625</td>\n",
       "      <td>0.276875</td>\n",
       "      <td>0.325792</td>\n",
       "      <td>0.366250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-27</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.114250</td>\n",
       "      <td>1.130208</td>\n",
       "      <td>0.275958</td>\n",
       "      <td>0.220958</td>\n",
       "      <td>0.129042</td>\n",
       "      <td>3.240458</td>\n",
       "      <td>2.859417</td>\n",
       "      <td>0.347042</td>\n",
       "      <td>0.844292</td>\n",
       "      <td>...</td>\n",
       "      <td>0.387542</td>\n",
       "      <td>0.271125</td>\n",
       "      <td>0.021042</td>\n",
       "      <td>1.155583</td>\n",
       "      <td>0.599708</td>\n",
       "      <td>0.865417</td>\n",
       "      <td>0.040750</td>\n",
       "      <td>0.292458</td>\n",
       "      <td>0.287458</td>\n",
       "      <td>0.511625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-28</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.115958</td>\n",
       "      <td>1.087667</td>\n",
       "      <td>0.316417</td>\n",
       "      <td>0.478667</td>\n",
       "      <td>0.103792</td>\n",
       "      <td>3.336417</td>\n",
       "      <td>2.691667</td>\n",
       "      <td>0.261583</td>\n",
       "      <td>0.447042</td>\n",
       "      <td>...</td>\n",
       "      <td>0.349333</td>\n",
       "      <td>0.045125</td>\n",
       "      <td>0.020500</td>\n",
       "      <td>0.597792</td>\n",
       "      <td>1.556708</td>\n",
       "      <td>0.638792</td>\n",
       "      <td>0.038958</td>\n",
       "      <td>0.213125</td>\n",
       "      <td>0.235708</td>\n",
       "      <td>0.498042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29</th>\n",
       "      <td>0.262000</td>\n",
       "      <td>0.116167</td>\n",
       "      <td>0.928208</td>\n",
       "      <td>0.262750</td>\n",
       "      <td>0.444292</td>\n",
       "      <td>0.148417</td>\n",
       "      <td>3.481667</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>0.404083</td>\n",
       "      <td>0.786333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.342167</td>\n",
       "      <td>0.045042</td>\n",
       "      <td>0.020583</td>\n",
       "      <td>0.402208</td>\n",
       "      <td>0.281792</td>\n",
       "      <td>0.736917</td>\n",
       "      <td>0.038917</td>\n",
       "      <td>0.210375</td>\n",
       "      <td>0.200708</td>\n",
       "      <td>0.500333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30</th>\n",
       "      <td>0.049375</td>\n",
       "      <td>0.114875</td>\n",
       "      <td>1.115000</td>\n",
       "      <td>0.325542</td>\n",
       "      <td>0.433208</td>\n",
       "      <td>0.153667</td>\n",
       "      <td>3.418458</td>\n",
       "      <td>1.526958</td>\n",
       "      <td>0.453750</td>\n",
       "      <td>0.546667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.379167</td>\n",
       "      <td>0.320083</td>\n",
       "      <td>0.021000</td>\n",
       "      <td>1.577417</td>\n",
       "      <td>0.149375</td>\n",
       "      <td>1.053792</td>\n",
       "      <td>0.074458</td>\n",
       "      <td>0.247208</td>\n",
       "      <td>0.238250</td>\n",
       "      <td>0.540667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>0.053833</td>\n",
       "      <td>0.118292</td>\n",
       "      <td>1.009083</td>\n",
       "      <td>0.372625</td>\n",
       "      <td>0.347458</td>\n",
       "      <td>0.327750</td>\n",
       "      <td>3.376000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>0.305417</td>\n",
       "      <td>0.845167</td>\n",
       "      <td>...</td>\n",
       "      <td>0.345417</td>\n",
       "      <td>0.278292</td>\n",
       "      <td>0.021208</td>\n",
       "      <td>0.846375</td>\n",
       "      <td>0.026583</td>\n",
       "      <td>0.946125</td>\n",
       "      <td>0.127625</td>\n",
       "      <td>0.241958</td>\n",
       "      <td>0.224792</td>\n",
       "      <td>0.520167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 499 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0         1         2         3         4         5    \\\n",
       "date                                                                     \n",
       "2019-01-01  0.107208  0.119875  0.932625  0.432833  0.580750  0.155292   \n",
       "2019-01-02  0.145333  0.112542  1.224708  0.394375  0.312583  0.201458   \n",
       "2019-01-03  0.453875  0.297750  1.159792  0.532375  0.418292  0.300583   \n",
       "2019-01-04  0.640625  0.288208  1.158750  0.387958  0.459500  0.205417   \n",
       "2019-01-05  0.482958  0.285708  1.099417  0.519792  0.191208  0.213208   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2019-12-27  0.000000  0.114250  1.130208  0.275958  0.220958  0.129042   \n",
       "2019-12-28  0.000000  0.115958  1.087667  0.316417  0.478667  0.103792   \n",
       "2019-12-29  0.262000  0.116167  0.928208  0.262750  0.444292  0.148417   \n",
       "2019-12-30  0.049375  0.114875  1.115000  0.325542  0.433208  0.153667   \n",
       "2019-12-31  0.053833  0.118292  1.009083  0.372625  0.347458  0.327750   \n",
       "\n",
       "                 6         7         8         9    ...       489       490  \\\n",
       "date                                                ...                       \n",
       "2019-01-01  3.258292  3.083333  0.310667  0.663500  ...  0.484083  0.090833   \n",
       "2019-01-02  3.240833  3.125000  0.359667  1.056167  ...  0.535167  0.207208   \n",
       "2019-01-03  3.323167  2.791667  0.331083  0.842958  ...  0.588833  0.226792   \n",
       "2019-01-04  3.309583  2.875000  0.280542  1.194917  ...  0.486542  0.215167   \n",
       "2019-01-05  3.298625  2.833333  0.221875  0.951708  ...  0.376167  0.086333   \n",
       "...              ...       ...       ...       ...  ...       ...       ...   \n",
       "2019-12-27  3.240458  2.859417  0.347042  0.844292  ...  0.387542  0.271125   \n",
       "2019-12-28  3.336417  2.691667  0.261583  0.447042  ...  0.349333  0.045125   \n",
       "2019-12-29  3.481667  2.900000  0.404083  0.786333  ...  0.342167  0.045042   \n",
       "2019-12-30  3.418458  1.526958  0.453750  0.546667  ...  0.379167  0.320083   \n",
       "2019-12-31  3.376000  1.250000  0.305417  0.845167  ...  0.345417  0.278292   \n",
       "\n",
       "                 491       492       493       494       495       496  \\\n",
       "date                                                                     \n",
       "2019-01-01  0.017417  0.257833  0.008958  0.876458  0.107708  0.249500   \n",
       "2019-01-02  0.017833  1.256625  0.008792  1.031042  0.043375  0.256208   \n",
       "2019-01-03  0.018042  1.335750  0.008917  1.105083  0.041417  0.277292   \n",
       "2019-01-04  0.017917  1.038750  0.008875  1.100333  0.038833  0.317458   \n",
       "2019-01-05  0.017625  0.543000  0.363250  1.001625  0.038625  0.276875   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2019-12-27  0.021042  1.155583  0.599708  0.865417  0.040750  0.292458   \n",
       "2019-12-28  0.020500  0.597792  1.556708  0.638792  0.038958  0.213125   \n",
       "2019-12-29  0.020583  0.402208  0.281792  0.736917  0.038917  0.210375   \n",
       "2019-12-30  0.021000  1.577417  0.149375  1.053792  0.074458  0.247208   \n",
       "2019-12-31  0.021208  0.846375  0.026583  0.946125  0.127625  0.241958   \n",
       "\n",
       "                 497       498  \n",
       "date                            \n",
       "2019-01-01  0.280333  0.341458  \n",
       "2019-01-02  0.329125  0.364458  \n",
       "2019-01-03  0.325250  0.367042  \n",
       "2019-01-04  0.329500  0.371917  \n",
       "2019-01-05  0.325792  0.366250  \n",
       "...              ...       ...  \n",
       "2019-12-27  0.287458  0.511625  \n",
       "2019-12-28  0.235708  0.498042  \n",
       "2019-12-29  0.200708  0.500333  \n",
       "2019-12-30  0.238250  0.540667  \n",
       "2019-12-31  0.224792  0.520167  \n",
       "\n",
       "[365 rows x 499 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consumptions_daily_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example Plot for Daily Mean Consumption of Customer #20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "customer = 20\n",
    "_ = consumptions_daily_mean.loc[:, customer].plot(color='b', alpha=0.7, \n",
    "                                                  ylabel='energy consumption in kWh per hour',\n",
    "                                                  title='Daily Mean Consumption of Customer #' + str(customer))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating the Daily Mean for the Weather Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_daily_mean = pd.DataFrame(weather.groupby(weather.index.date).mean(), index=days.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataframe *weather_daily_mean* contains the daily mean values for the weather data (outside temperature) of all customers. This means the table contains **499** columns (customers) and **365** rows (days within the measurement period)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>489</th>\n",
       "      <th>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>12.328750</td>\n",
       "      <td>12.363750</td>\n",
       "      <td>5.349583</td>\n",
       "      <td>5.397500</td>\n",
       "      <td>13.213750</td>\n",
       "      <td>12.522083</td>\n",
       "      <td>12.431667</td>\n",
       "      <td>4.043750</td>\n",
       "      <td>5.355417</td>\n",
       "      <td>12.523750</td>\n",
       "      <td>...</td>\n",
       "      <td>12.446667</td>\n",
       "      <td>12.350417</td>\n",
       "      <td>12.591667</td>\n",
       "      <td>8.065833</td>\n",
       "      <td>8.027917</td>\n",
       "      <td>8.072917</td>\n",
       "      <td>4.481250</td>\n",
       "      <td>4.748333</td>\n",
       "      <td>4.748333</td>\n",
       "      <td>4.748333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>14.049583</td>\n",
       "      <td>14.085417</td>\n",
       "      <td>5.789167</td>\n",
       "      <td>5.838750</td>\n",
       "      <td>14.077083</td>\n",
       "      <td>14.242500</td>\n",
       "      <td>14.152083</td>\n",
       "      <td>5.395417</td>\n",
       "      <td>5.795833</td>\n",
       "      <td>13.386667</td>\n",
       "      <td>...</td>\n",
       "      <td>13.310000</td>\n",
       "      <td>13.039167</td>\n",
       "      <td>13.280417</td>\n",
       "      <td>9.103750</td>\n",
       "      <td>9.065417</td>\n",
       "      <td>9.110417</td>\n",
       "      <td>4.233750</td>\n",
       "      <td>5.606667</td>\n",
       "      <td>5.606667</td>\n",
       "      <td>5.606667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>14.383333</td>\n",
       "      <td>14.418333</td>\n",
       "      <td>6.513750</td>\n",
       "      <td>6.562083</td>\n",
       "      <td>14.542500</td>\n",
       "      <td>14.576667</td>\n",
       "      <td>14.485833</td>\n",
       "      <td>5.731250</td>\n",
       "      <td>6.519583</td>\n",
       "      <td>13.852917</td>\n",
       "      <td>...</td>\n",
       "      <td>13.775000</td>\n",
       "      <td>13.766250</td>\n",
       "      <td>14.007500</td>\n",
       "      <td>9.659583</td>\n",
       "      <td>9.622917</td>\n",
       "      <td>9.667917</td>\n",
       "      <td>5.296667</td>\n",
       "      <td>6.423750</td>\n",
       "      <td>6.423750</td>\n",
       "      <td>6.423750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>14.302917</td>\n",
       "      <td>14.338333</td>\n",
       "      <td>7.785417</td>\n",
       "      <td>7.833750</td>\n",
       "      <td>14.669583</td>\n",
       "      <td>14.497083</td>\n",
       "      <td>14.405833</td>\n",
       "      <td>7.046250</td>\n",
       "      <td>7.792500</td>\n",
       "      <td>13.979167</td>\n",
       "      <td>...</td>\n",
       "      <td>13.902500</td>\n",
       "      <td>13.527083</td>\n",
       "      <td>13.769583</td>\n",
       "      <td>9.515417</td>\n",
       "      <td>9.477500</td>\n",
       "      <td>9.522500</td>\n",
       "      <td>5.637917</td>\n",
       "      <td>7.262500</td>\n",
       "      <td>7.262500</td>\n",
       "      <td>7.262500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>13.712500</td>\n",
       "      <td>13.747083</td>\n",
       "      <td>7.265000</td>\n",
       "      <td>7.313750</td>\n",
       "      <td>13.882917</td>\n",
       "      <td>13.905833</td>\n",
       "      <td>13.815000</td>\n",
       "      <td>7.292500</td>\n",
       "      <td>7.271250</td>\n",
       "      <td>13.191250</td>\n",
       "      <td>...</td>\n",
       "      <td>13.115833</td>\n",
       "      <td>13.099583</td>\n",
       "      <td>13.341250</td>\n",
       "      <td>9.204167</td>\n",
       "      <td>9.166667</td>\n",
       "      <td>9.211667</td>\n",
       "      <td>4.648333</td>\n",
       "      <td>6.455833</td>\n",
       "      <td>6.455833</td>\n",
       "      <td>6.455833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-27</th>\n",
       "      <td>16.589583</td>\n",
       "      <td>16.623333</td>\n",
       "      <td>12.034167</td>\n",
       "      <td>12.082917</td>\n",
       "      <td>16.086250</td>\n",
       "      <td>16.692500</td>\n",
       "      <td>16.649167</td>\n",
       "      <td>11.165417</td>\n",
       "      <td>12.040417</td>\n",
       "      <td>15.467083</td>\n",
       "      <td>...</td>\n",
       "      <td>15.393750</td>\n",
       "      <td>15.867083</td>\n",
       "      <td>16.108750</td>\n",
       "      <td>12.208750</td>\n",
       "      <td>12.175417</td>\n",
       "      <td>12.215417</td>\n",
       "      <td>9.508750</td>\n",
       "      <td>10.398333</td>\n",
       "      <td>10.398333</td>\n",
       "      <td>10.398333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-28</th>\n",
       "      <td>16.762917</td>\n",
       "      <td>16.797500</td>\n",
       "      <td>13.096250</td>\n",
       "      <td>13.143750</td>\n",
       "      <td>16.247083</td>\n",
       "      <td>16.866667</td>\n",
       "      <td>16.822083</td>\n",
       "      <td>11.322917</td>\n",
       "      <td>13.101250</td>\n",
       "      <td>15.627083</td>\n",
       "      <td>...</td>\n",
       "      <td>15.554167</td>\n",
       "      <td>16.672500</td>\n",
       "      <td>16.915000</td>\n",
       "      <td>12.948333</td>\n",
       "      <td>12.915000</td>\n",
       "      <td>12.955000</td>\n",
       "      <td>10.972500</td>\n",
       "      <td>11.366250</td>\n",
       "      <td>11.366250</td>\n",
       "      <td>11.366250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29</th>\n",
       "      <td>16.808333</td>\n",
       "      <td>16.842500</td>\n",
       "      <td>13.534583</td>\n",
       "      <td>13.583750</td>\n",
       "      <td>16.498333</td>\n",
       "      <td>16.912500</td>\n",
       "      <td>16.867083</td>\n",
       "      <td>11.932917</td>\n",
       "      <td>13.541250</td>\n",
       "      <td>15.879583</td>\n",
       "      <td>...</td>\n",
       "      <td>15.805417</td>\n",
       "      <td>14.752083</td>\n",
       "      <td>14.994583</td>\n",
       "      <td>11.788750</td>\n",
       "      <td>11.755417</td>\n",
       "      <td>11.795417</td>\n",
       "      <td>8.668750</td>\n",
       "      <td>11.740833</td>\n",
       "      <td>11.740833</td>\n",
       "      <td>11.740833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30</th>\n",
       "      <td>16.168750</td>\n",
       "      <td>16.202500</td>\n",
       "      <td>10.853333</td>\n",
       "      <td>10.902917</td>\n",
       "      <td>15.985000</td>\n",
       "      <td>16.271667</td>\n",
       "      <td>16.227500</td>\n",
       "      <td>9.983333</td>\n",
       "      <td>10.860000</td>\n",
       "      <td>15.364583</td>\n",
       "      <td>...</td>\n",
       "      <td>15.292083</td>\n",
       "      <td>15.672917</td>\n",
       "      <td>15.914583</td>\n",
       "      <td>11.605417</td>\n",
       "      <td>11.572083</td>\n",
       "      <td>11.612083</td>\n",
       "      <td>9.310833</td>\n",
       "      <td>9.405833</td>\n",
       "      <td>9.405833</td>\n",
       "      <td>9.405833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>16.911250</td>\n",
       "      <td>16.945833</td>\n",
       "      <td>13.942083</td>\n",
       "      <td>13.990833</td>\n",
       "      <td>16.502917</td>\n",
       "      <td>17.014167</td>\n",
       "      <td>16.972083</td>\n",
       "      <td>12.518750</td>\n",
       "      <td>13.948333</td>\n",
       "      <td>15.889167</td>\n",
       "      <td>...</td>\n",
       "      <td>15.816667</td>\n",
       "      <td>15.839167</td>\n",
       "      <td>16.074167</td>\n",
       "      <td>12.520833</td>\n",
       "      <td>12.487083</td>\n",
       "      <td>12.527500</td>\n",
       "      <td>10.239583</td>\n",
       "      <td>11.876250</td>\n",
       "      <td>11.876250</td>\n",
       "      <td>11.876250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 499 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0          1          2          3          4          5    \\\n",
       "date                                                                           \n",
       "2019-01-01  12.328750  12.363750   5.349583   5.397500  13.213750  12.522083   \n",
       "2019-01-02  14.049583  14.085417   5.789167   5.838750  14.077083  14.242500   \n",
       "2019-01-03  14.383333  14.418333   6.513750   6.562083  14.542500  14.576667   \n",
       "2019-01-04  14.302917  14.338333   7.785417   7.833750  14.669583  14.497083   \n",
       "2019-01-05  13.712500  13.747083   7.265000   7.313750  13.882917  13.905833   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "2019-12-27  16.589583  16.623333  12.034167  12.082917  16.086250  16.692500   \n",
       "2019-12-28  16.762917  16.797500  13.096250  13.143750  16.247083  16.866667   \n",
       "2019-12-29  16.808333  16.842500  13.534583  13.583750  16.498333  16.912500   \n",
       "2019-12-30  16.168750  16.202500  10.853333  10.902917  15.985000  16.271667   \n",
       "2019-12-31  16.911250  16.945833  13.942083  13.990833  16.502917  17.014167   \n",
       "\n",
       "                  6          7          8          9    ...        489  \\\n",
       "date                                                    ...              \n",
       "2019-01-01  12.431667   4.043750   5.355417  12.523750  ...  12.446667   \n",
       "2019-01-02  14.152083   5.395417   5.795833  13.386667  ...  13.310000   \n",
       "2019-01-03  14.485833   5.731250   6.519583  13.852917  ...  13.775000   \n",
       "2019-01-04  14.405833   7.046250   7.792500  13.979167  ...  13.902500   \n",
       "2019-01-05  13.815000   7.292500   7.271250  13.191250  ...  13.115833   \n",
       "...               ...        ...        ...        ...  ...        ...   \n",
       "2019-12-27  16.649167  11.165417  12.040417  15.467083  ...  15.393750   \n",
       "2019-12-28  16.822083  11.322917  13.101250  15.627083  ...  15.554167   \n",
       "2019-12-29  16.867083  11.932917  13.541250  15.879583  ...  15.805417   \n",
       "2019-12-30  16.227500   9.983333  10.860000  15.364583  ...  15.292083   \n",
       "2019-12-31  16.972083  12.518750  13.948333  15.889167  ...  15.816667   \n",
       "\n",
       "                  490        491        492        493        494        495  \\\n",
       "date                                                                           \n",
       "2019-01-01  12.350417  12.591667   8.065833   8.027917   8.072917   4.481250   \n",
       "2019-01-02  13.039167  13.280417   9.103750   9.065417   9.110417   4.233750   \n",
       "2019-01-03  13.766250  14.007500   9.659583   9.622917   9.667917   5.296667   \n",
       "2019-01-04  13.527083  13.769583   9.515417   9.477500   9.522500   5.637917   \n",
       "2019-01-05  13.099583  13.341250   9.204167   9.166667   9.211667   4.648333   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "2019-12-27  15.867083  16.108750  12.208750  12.175417  12.215417   9.508750   \n",
       "2019-12-28  16.672500  16.915000  12.948333  12.915000  12.955000  10.972500   \n",
       "2019-12-29  14.752083  14.994583  11.788750  11.755417  11.795417   8.668750   \n",
       "2019-12-30  15.672917  15.914583  11.605417  11.572083  11.612083   9.310833   \n",
       "2019-12-31  15.839167  16.074167  12.520833  12.487083  12.527500  10.239583   \n",
       "\n",
       "                  496        497        498  \n",
       "date                                         \n",
       "2019-01-01   4.748333   4.748333   4.748333  \n",
       "2019-01-02   5.606667   5.606667   5.606667  \n",
       "2019-01-03   6.423750   6.423750   6.423750  \n",
       "2019-01-04   7.262500   7.262500   7.262500  \n",
       "2019-01-05   6.455833   6.455833   6.455833  \n",
       "...               ...        ...        ...  \n",
       "2019-12-27  10.398333  10.398333  10.398333  \n",
       "2019-12-28  11.366250  11.366250  11.366250  \n",
       "2019-12-29  11.740833  11.740833  11.740833  \n",
       "2019-12-30   9.405833   9.405833   9.405833  \n",
       "2019-12-31  11.876250  11.876250  11.876250  \n",
       "\n",
       "[365 rows x 499 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_daily_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example Plot for Daily Mean of Weather Data of Customer #20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "customer = 20\n",
    "_ = weather_daily_mean.loc[:, customer].plot(color='orange', alpha=0.7, ylabel='outside temperature in °C',\n",
    "                                 title='Weather (Outside Temperature) of Customer #' + str(customer))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}