{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of contents\n",
    "1. [Settings](#settings)\n",
    "2. [Download](#download)\n",
    "3. [Preprocessing](#pre)\n",
    "4. [Heat demand time series](#demand)\n",
    "5. [COP time series](#cop)\n",
    "6. [Writing](#write)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=settings></a>\n",
    "# 1. Settings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "I recommend running this notebook in a [conda environment, which can be created from the environment.yml file](https://conda.io/docs/user-guide/tasks/manage-environments.html#creating-an-environment-from-an-environment-yml-file) provided with this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Python libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Python modules\n",
    "import os\n",
    "import shutil\n",
    "import pandas as pd\n",
    "from time import time\n",
    "from datetime import date\n",
    "import datetime\n",
    "\n",
    "# Custom scripts\n",
    "import scripts.download as download \n",
    "import scripts.read as read\n",
    "import scripts.preprocess as preprocess\n",
    "import scripts.demand as demand\n",
    "import scripts.cop as cop\n",
    "import scripts.write as write\n",
    "import scripts.metadata as metadata\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Version and changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "version = '2022-02-22'\n",
    "changes = 'Update and extension'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "home_path = os.path.realpath('.')\n",
    "\n",
    "input_path = os.path.join(home_path, 'input')\n",
    "interim_path = os.path.join(home_path, 'interim')\n",
    "output_path = os.path.join(home_path, 'output', version)\n",
    "\n",
    "for path in [input_path, interim_path, output_path]:\n",
    "    os.makedirs(path, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select geographical and temporal scope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_countries = ['AT', 'BE', 'BG', 'CZ', 'CH', 'DE', 'DK', \n",
    "                 'EE', 'ES', 'FI', 'FR', 'GB', 'GR', 'HR', \n",
    "                 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'NL', \n",
    "                 'NO', 'PL', 'PT', 'RO', 'SE', 'SI', 'SK'] # available\n",
    "countries = [\"DE\"]  # selected for calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_start = 2008\n",
    "year_end = 2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set ECMWF access key\n",
    "In the following, this notebook downloads weather data from the ECMWF server. For accessing this server, follow the steps below:\n",
    "1.  Register at https://apps.ecmwf.int/registration/.\n",
    "2.  Login at https://apps.ecmwf.int/auth/login/.\n",
    "3.  Retrieve your key at https://api.ecmwf.int/v1/key/.\n",
    "4.  Enter your key and your e-mail below.\n",
    "\n",
    "If you have already [installed](https://confluence.ecmwf.int/display/WEBAPI/Access+ECMWF+Public+Datasets#AccessECMWFPublicDatasets-key) your ECMWF KEY, this step is skipped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#if not os.path.isfile(os.path.join(os.environ[\"USERPROFILE\"], \".ecmwfapirc\")):\n",
    " #   os.environ[\"ECMWF_API_URL\"] = \"https://api.ecmwf.int/v1\"\n",
    "  #  os.environ[\"ECMWF_API_KEY\"] = \"XXXXXXXXX\"\n",
    "   # os.environ[\"ECMWF_API_EMAIL\"] = \"jsmith@institution.org\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=download></a>\n",
    "# 2. Download\n",
    "In the following, weather and population data is downloaded from the respective sources. For all years and countries, this takes around 45 minutes to run.\n",
    "\n",
    "Note that standard load profile parameters from [BGW](http://www.gwb-netz.de/wa_files/05_bgw_leitfaden_lastprofile_56550.pdf)/[BDEW](https://www.enwg-veroeffentlichungen.de/badtoelz/Netze/Gasnetz/Netzbeschreibung/LF-Abwicklung-von-Standardlastprofilen-Gas-20110630-final.pdf) are already provided with in the input directory. Energy statistics can be downloaded from the [JRC-IDEES](https://data.jrc.ec.europa.eu/dataset/jrc-10110-10001) and preprocessed with the <a href=\"preprocessing/preprocessing_JRC_IDEES.ipynb\">Processing Notebook</a></li>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weather data\n",
    "As mentioned above, weather data is downloaded from ECMWF, more specifically form the ERA-5 archive (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5). The following data is retrieved:\n",
    "* Wind: wind speed at 10 m above ground for heating seasons (October-April) in 1979-2017 in monthly resolution \n",
    "* Temperature: ambient air temperature at 2 m above ground and soil temperature (0-7cm below ground) for the selected years in hourly resolution\n",
    "\n",
    "All data is downloaded for the whole of Europe. If some data already exists on your computer, this data will be skipped in the download process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Download successful\n"
     ]
    }
   ],
   "source": [
    "download.wind(input_path) \n",
    "download.temperatures(input_path, year_start, year_end)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Population data\n",
    "As mentioned above, population data is downloaded from [EUROSTAT](http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/geostat)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Download successful\n"
     ]
    }
   ],
   "source": [
    "download.population(input_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=pre></a>\n",
    "# 3. Preprocessing\n",
    "Population and weather data is preprocessed. This takes around 10 minutes to run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Re-mapping population data\n",
    "The population data from Eurostat features a 1 km² grid, which country-by-country transformed to the 0.75 x 0.75° grid of the weather data in the following. Interim results are saved/loaded from disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/Jarusch/Documents/Hertie/when2heat_europe/interim/population_DE already exists and is read from disk.\n",
      "Plot of the re-mapped population data of DE (first selected country) for visual inspection:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mapped_population = preprocess.map_population(input_path, countries, interim_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing weather data\n",
    "\n",
    "The temporal resolution of wind data is changed as follows:\n",
    "* From monthly to the average of all heating periods from 1979 to 2016\n",
    "\n",
    "To speed up the calculation, all weather data is filtered by the selected countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot of the wind averages for visual inspection:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wind = preprocess.wind(input_path, mapped_population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "temperature = preprocess.temperature(input_path, year_start, year_start, mapped_population) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=demand></a>\n",
    "# 4. Heat demand time series\n",
    "For all years and countries, the calculation of heat demand time series takes around 20 minutes to run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reference temperature\n",
    "\n",
    "To capture the thermal inertia of buildings, the daily reference temperature is calculated as the weighted mean of the ambient air temperature of the actual and the three preceding days. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "reference_temperature = demand.reference_temperature(temperature['air'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heating threshold temperature adjustment\n",
    "Reference temperatures are adjusted by differences in the estimated national heating threshold (Kozarcanin et al. 2020) as compared to Germany."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "heating_thresholds = read.heating_thresholds(input_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "adjusted_temperature = demand.adjust_temperature(reference_temperature, heating_thresholds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Daily demand\n",
    "\n",
    "Daily demand factors are derived from the reference temperatures using profile functions as described in [BDEW](https://www.enwg-veroeffentlichungen.de/badtoelz/Netze/Gasnetz/Netzbeschreibung/LF-Abwicklung-von-Standardlastprofilen-Gas-20110630-final.pdf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_parameters = read.daily_parameters(input_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_heat = demand.daily_heat(adjusted_temperature,\n",
    "                               wind, \n",
    "                               daily_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_water = demand.daily_water(adjusted_temperature,\n",
    "                                 wind,\n",
    "                                 daily_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hourly demand\n",
    "\n",
    "Hourly damand factors are calculated from the daily demand based on hourly factors from [BGW](http://www.gwb-netz.de/wa_files/05_bgw_leitfaden_lastprofile_56550.pdf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "hourly_parameters = read.hourly_parameters(input_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Jarusch/Documents/Hertie/when2heat_europe/scripts/demand.py:134: FutureWarning: The 'lookup' method is deprecated and will beremoved in a future version.You can use DataFrame.melt and DataFrame.locas a substitute.\n",
      "  slp[column] = parameters[building].lookup(times, classes.loc[:, column])\n"
     ]
    }
   ],
   "source": [
    "a = demand.hourly_heat(daily_heat,\n",
    "                                 reference_temperature, \n",
    "                                 hourly_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "hourly_heat = demand.hourly_heat(daily_heat,\n",
    "                                 reference_temperature, \n",
    "                                 hourly_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "hourly_water = demand.hourly_water(daily_water,\n",
    "                                   reference_temperature, \n",
    "                                   hourly_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "hourly_space = (hourly_heat - hourly_water).clip(lower=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight and scale\n",
    "The spatial time series are weighted with the population and normalized to 1 TWh yearly demand each. For years included in the [JRC-IDEES](https://data.jrc.ec.europa.eu/dataset/jrc-10110-10001), time series are scaled according to the there reported annual values. The time series are not yet spatially aggregated because spatial time series are needed for COP calculation. To generate these annual heat demands use our <a href=\"preprocessing/preprocessing_JRC_IDEES.ipynb\">Processing Notebook</a></li>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "building_database = read.building_database(input_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "spatial_space = demand.finishing(hourly_space, mapped_population, building_database['space'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "spatial_water = demand.finishing(hourly_water, mapped_population, building_database['water'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Safepoint\n",
    "\n",
    "The following cells can be used to save and reload the spatial hourly time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "spatial_space.to_pickle(os.path.join(interim_path, 'spatial_space'))\n",
    "spatial_water.to_pickle(os.path.join(interim_path, 'spatial_water'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "spatial_space = pd.read_pickle(os.path.join(interim_path, 'spatial_space'))[countries]\n",
    "spatial_water = pd.read_pickle(os.path.join(interim_path, 'spatial_water'))[countries]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregate and combine\n",
    "All heat demand time series are aggregated country-wise and combined into one data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_heat = demand.combine(spatial_space, spatial_water)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=cop></a>\n",
    "# 5. COP time series\n",
    "For all years and countries, the calculation of the coefficient of performance (COP) of heat pumps takes around 5 minutes to run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Source temperature \n",
    "For air-sourced, ground-sources and groundwater-sourced heat pumps (ASHP, GSHP and WSHP), the relevant heat source temperatures are calculated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_temperature = cop.source_temperature(temperature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sink temperatures\n",
    "Heat sink temperatures, i.e. the temperature level at which the heat pumps have to provide heat, are calculated for floor heating, radiator heating and warm water."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "sink_temperature = cop.sink_temperature(temperature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## COP\n",
    "The COP is derived from the temperature difference between heat sources and sinks using COP curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "cop_parameters = read.cop_parameters(input_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "spatial_cop = cop.spatial_cop(source_temperature, sink_temperature, cop_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Safepoint\n",
    "\n",
    "The following cells can be used to save and reload the spatial hourly time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "spatial_cop.to_pickle(os.path.join(interim_path, 'spatial_cop'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "spatial_cop = pd.read_pickle(os.path.join(interim_path, 'spatial_cop'))[countries]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregating and correction\n",
    "The spatial COP time series are weighted with the spatial heat demand and aggregated into national time series. The national time series are corrected for part-load losses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_cop = cop.finishing(spatial_cop, spatial_space, spatial_water)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## COP averages\n",
    "COP averages (performance factors) are calculated and saved to disk for validation purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cop.validation(final_cop, final_heat, interim_path, 'corrected')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cop.validation(cop.finishing(spatial_cop, spatial_space, spatial_water, correction=1),\n",
    "               #final_heat, interim_path, \"uncorrected\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=write></a>\n",
    "# 6. Writing\n",
    "For data and metadata, this takes around 5 minutes to run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "As for the OPSD \"Time Series\" package, data are provided in three different \"shapes\":\n",
    "\n",
    "* SingleIndex (easy to read for humans, compatible with datapackage standard, small file size)\n",
    "  * Fileformat: CSV, SQLite\n",
    "* MultiIndex (easy to read into GAMS, not compatible with datapackage standard, small file size)\n",
    "  * Fileformat: CSV, Excel\n",
    "* Stacked (compatible with data package standard, large file size, many rows, too many for Excel)\n",
    "  * Fileformat: CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The different shapes are created before they are saved to files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "shaped_dfs = write.shaping(final_heat, final_cop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write data to an SQL-database, ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "write.to_sql(shaped_dfs, output_path, home_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and to CSV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "write.to_csv(shaped_dfs, output_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Writing to Excel takes extremely long. As a workaround, a copy of the multi-indexed data is writtten to CSV and manually converted to Excel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metadata\n",
    "The metadata is reported in a JSON file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata.make_json(shaped_dfs, version, changes, year_start, year_end, output_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Copy input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shutil.copytree(input_path, os.path.join(output_path, 'original_data'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checksums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata.checksums(output_path, home_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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