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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prediction Using Different Machine Learning Methods\n",
"## Random Forests"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook, we'll train a Random Forest Regression model for predicting building energy consumption based on historical enregy data and several weather variables. We'll use daily energy data and weather data to predict energy consumption."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline \n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"pd.options.display.mpl_style = 'default'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import seaborn as sns\n",
"import scipy as sp\n",
"import sklearn\n",
"import sklearn.cross_validation\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.cross_validation import cross_val_score\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook, we'll train a Random Forest Regression model for predicting building energy consumption based on historical enregy data and several weather variables. We'll use daily energy data and weather data to predict energy consumption."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# read in original data:\n",
"electricity = pd.read_excel('Data/dailyElectricityWithFeatures.xlsx')\n",
"electricity = electricity.drop('startDay', 1).drop('endDay', 1)\n",
"#electricity = electricity.drop('humidityRatio-kg/kg',1).drop('coolingDegrees',1).drop('heatingDegrees',1).drop('dehumidification',1).drop('occupancy',1)\n",
"electricity = electricity.dropna()\n",
"\n",
"chilledWater = pd.read_excel('Data/dailyChilledWaterWithFeatures.xlsx')\n",
"chilledWater = chilledWater.drop('startDay', 1).drop('endDay', 1)\n",
"chilledWater = chilledWater.dropna()\n",
"\n",
"steam = pd.read_excel('Data/dailySteamWithFeatures.xlsx')\n",
"steam = steam.drop('startDay', 1).drop('endDay', 1)\n",
"steam = steam.dropna()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# normalize data:\n",
"normalized_electricity = electricity - electricity.mean()\n",
"normalized_chilledWater = chilledWater - chilledWater.mean()\n",
"normalized_steam = steam - steam.mean()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Adding a new column to specify if working days or weekends and holidays. We'll set working days to 0, and weekends and holidays to 1. US public holidays are listed here, http://www.officeholidays.com/countries/usa/ We may also remove vacations times when there is no school, but we'll do it since we don't have this information."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Initialization all days to 0\n",
"normalized_electricity['day_type'] = np.zeros(len(normalized_electricity))\n",
"normalized_chilledWater['day_type'] = np.zeros(len(normalized_chilledWater))\n",
"normalized_steam['day_type'] = np.zeros(len(normalized_steam))\n",
"\n",
"# Set weekends to 1\n",
"normalized_electricity['day_type'][(normalized_electricity.index.dayofweek==5)|(normalized_electricity.index.dayofweek==6)] = 1\n",
"normalized_chilledWater['day_type'][(normalized_chilledWater.index.dayofweek==5)|(normalized_chilledWater.index.dayofweek==6)] = 1\n",
"normalized_steam['day_type'][(normalized_steam.index.dayofweek==5)|(normalized_steam.index.dayofweek==6)] = 1\n",
"\n",
"# Set holidays to 1\n",
"holidays = ['2014-01-01','2014-01-20','2014-05-26','2014-07-04','2014-09-01','2014-11-11','2014-11-27','2014-12-25','2013-01-01',\n",
" '2013-01-21','2013-05-27','2013-07-04','2013-09-02','2013-11-11','2013-11-27','2013-12-25','2012-01-01','2012-01-16',\n",
" '2012-05-28','2012-07-04','2012-09-03','2012-11-12','2012-11-22','2012-12-25']\n",
"\n",
"for i in range(len(holidays)):\n",
" normalized_electricity['day_type'][normalized_electricity.index.date==np.datetime64(holidays[i])] = 1\n",
" normalized_chilledWater['day_type'][normalized_chilledWater.index.date==np.datetime64(holidays[i])] = 1\n",
" normalized_steam['day_type'][normalized_steam.index.date==np.datetime64(holidays[i])] = 1"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Analysis of electricity data."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Split train and test data:\n",
"elect_train = pd.DataFrame(data=normalized_electricity, index=np.arange('2012-01', '2014-01', dtype='datetime64[D]')).dropna()\n",
"elect_test = pd.DataFrame(data=normalized_electricity, index=np.arange('2014-01', '2014-11', dtype='datetime64[D]')).dropna()\n",
"\n",
"XX_elect_train = elect_train.drop('electricity-kWh', axis = 1).reset_index().drop('index', axis = 1)\n",
"XX_elect_test = elect_test.drop('electricity-kWh', axis = 1).reset_index().drop('index', axis = 1)\n",
"\n",
"YY_elect_train = elect_train['electricity-kWh']\n",
"YY_elect_test = elect_test['electricity-kWh']\n",
"\n",
"print XX_elect_train.shape, XX_elect_test.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(634, 13) (294, 13)\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"XX_elect_train.head()"
],
"language": "python",
"metadata": {},
"outputs": [
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