create_epw.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Library initialization and data import\n",
    "- Libraries needed for data preprocessing and missing data correction\n",
    "\n",
    "- Weihenstephan weather data from 2021 imported from DWD CDC server: \n",
    "https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/hourly/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-65-0a2497a5acbd>:60: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dict_dwd[name].iloc[:,i][dict_dwd[name].iloc[:,i] == -999]= pd.NA\n",
      "<ipython-input-65-0a2497a5acbd>:60: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dict_dwd[name].iloc[:,i][dict_dwd[name].iloc[:,i] == -999]= pd.NA\n",
      "<ipython-input-65-0a2497a5acbd>:60: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dict_dwd[name].iloc[:,i][dict_dwd[name].iloc[:,i] == -999]= pd.NA\n",
      "<ipython-input-65-0a2497a5acbd>:60: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dict_dwd[name].iloc[:,i][dict_dwd[name].iloc[:,i] == -999]= pd.NA\n",
      "<ipython-input-65-0a2497a5acbd>:60: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dict_dwd[name].iloc[:,i][dict_dwd[name].iloc[:,i] == -999]= pd.NA\n",
      "<ipython-input-65-0a2497a5acbd>:60: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dict_dwd[name].iloc[:,i][dict_dwd[name].iloc[:,i] == -999]= pd.NA\n",
      "<ipython-input-65-0a2497a5acbd>:60: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dict_dwd[name].iloc[:,i][dict_dwd[name].iloc[:,i] == -999]= pd.NA\n"
     ]
    }
   ],
   "source": [
    "import glob as gb\n",
    "#### Import data from DWD server\n",
    "path_dwd = ('files/dwd_weihenstephan/*.txt')\n",
    "pathlist_dwd = gb.glob(path_dwd)\n",
    "\n",
    "#time limit\n",
    "startdate = \"2021-01-01\"\n",
    "enddate = \"2021-12-31\"\n",
    "\n",
    "#Initialize dictionary\n",
    "dict_dwd = {}\n",
    "\n",
    "#Loop over txt files\n",
    "for p in pathlist_dwd:\n",
    "    \"\"\"\n",
    "    - n -\n",
    "    V_N_I = Sky cover index, boolean\n",
    "    V_N = Total sky cover, 1/8\n",
    "\n",
    "    - ff - \n",
    "    FF_10 = Average wind speed, in m/s\n",
    "    DD_10 = Average wind direction, in deg\n",
    "\n",
    "    - rr -\n",
    "    RWS_DAU_10 = Rain duration, in minutes\n",
    "    RWS_10 = Liquid precipitation depth, in mm\n",
    "    RWS_IND_10 = Rain indicator, boolean\n",
    "\n",
    "    - sd -\n",
    "    DS_10 = Diffuse horizontal radiation, in J/cm^2\n",
    "    GS_10 = Global horizontal radiation, in J/cm^2\n",
    "    SD_10 = Sunshine duration, in h\n",
    "    LS_10 = Longwave radiation, in J/cm^2\n",
    "\n",
    "    - tu -\n",
    "    PP_10 = Air pressure, in hPa\n",
    "    TT_10 = Dry-bulb temperature air, in °C\n",
    "    TM5_10 = Air temperature at 5 cm, in °C\n",
    "    RF_10 = Relative humidity, in %\n",
    "    TD_10 = Dew point temperature air, in °C\n",
    "    \"\"\"\n",
    "    if (p.split('_')[-4] == 'stunde'):\n",
    "        name = p.split('_')[-5]\n",
    "        keytxt = p.split('_')[2]\n",
    "    else:\n",
    "        name = p.split('_')[-4]\n",
    "        keytxt = p.split('_')[4]\n",
    "\n",
    "    dict_dwd[name] = pd.read_csv(p, sep=\";\", decimal  ='.')\n",
    "    dict_dwd[name].index = pd.to_datetime(dict_dwd[name].MESS_DATUM,format=\"%Y%m%d%H%M\")\n",
    "    dict_dwd[name] = dict_dwd[name].drop(columns =[\"STATIONS_ID\",\n",
    "                                                   \"MESS_DATUM\",\n",
    "                                                   dict_dwd[name].columns[dict_dwd[name].columns.str.contains(\"QN\")][0],\n",
    "                                                   \"eor\"])\n",
    "\n",
    "    dict_dwd[name] = dict_dwd[name][startdate:enddate]\n",
    "\n",
    "    #Correct missing values \n",
    "    for i in range(len(dict_dwd[name].columns)):\n",
    "        dict_dwd[name].iloc[:,i][dict_dwd[name].iloc[:,i] == -999]= pd.NA\n",
    "        dict_dwd[name].iloc[:,i] = dict_dwd[name].iloc[:,i].interpolate(method='linear', axis=0)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histogram for different measured variables. \n",
    "\n",
    "The goal is to understand the distribution of the online measured data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'PP_10'}>,\n",
       "        <AxesSubplot:title={'center':'TT_10'}>],\n",
       "       [<AxesSubplot:title={'center':'TM5_10'}>,\n",
       "        <AxesSubplot:title={'center':'RF_10'}>],\n",
       "       [<AxesSubplot:title={'center':'TD_10'}>, <AxesSubplot:>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dict_dwd['tu'].resample('H').mean().hist(bins=20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'DS_10'}>,\n",
       "        <AxesSubplot:title={'center':'GS_10'}>],\n",
       "       [<AxesSubplot:title={'center':'SD_10'}>,\n",
       "        <AxesSubplot:title={'center':'LS_10'}>]], dtype=object)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dict_dwd['sd'].resample('H').mean().hist(bins=20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# For the missing solar radiation, a kernel ridge regression is used to estimate the missing data. \n",
    "\n",
    "Different missing data periods are observed for global and diffuse radiation. \n",
    "\n",
    "The periodicity of the solar radiation every day and every year makes it suitable for RBF kernels (gaussian distribution against the hour, piecewise linear against the solar zenith angle, sigmoid shape against the dry-bulb temperature).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import kernel_ridge\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "'''\n",
    "Global solar radiation (GS) Missing data -> 2021-06-30:2021-07-11\n",
    "Diffuse solar radiation (DS) Missing data -> 2021-06-30:2021-07-26\n",
    "'''\n",
    "## Solar zenith angle calculation\n",
    "latitude = np.radians(48.4024)\n",
    "h = (dict_dwd['tu'].TT_10.resample('H').mean().index.hour -12)/12\n",
    "angle_declination = 23.45*np.sin(np.radians(360*(284+(dict_dwd['tu'].resample('H').mean().index.day_of_year-1))/365))\n",
    "solar_zenith_angle = np.sin(latitude)*np.sin(np.radians(angle_declination)) + \\\n",
    "    np.cos(latitude)*np.cos(np.radians(angle_declination))*np.cos(h)\n",
    "\n",
    "SZA = pd.DataFrame({'SZA': solar_zenith_angle})\n",
    "SZA.index = dict_dwd['tu'].TT_10.resample('H').mean().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x20746afb160>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter plot solar zenith angle vs. global radiation\n",
    "plt.scatter(SZA.SZA['2021-05':'2021-06'],\n",
    "            dict_dwd['sd'].GS_10.resample('H').mean()['2021-05':'2021-06'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1ab6260f1c0>"
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter plot temperature vs. global radiation\n",
    "plt.scatter(dict_dwd['tu'].TT_10.resample('H').mean()['2021-05':'2021-06'],\n",
    "            dict_dwd['sd'].GS_10.resample('H').mean()['2021-05':'2021-06'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1ab62660760>"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter plot hour of the day vs. global radiation\n",
    "plt.scatter(SZA.SZA['2021-05':'2021-06'].index.hour,\n",
    "            dict_dwd['sd'].GS_10.resample('H').mean()['2021-05':'2021-06'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is split in training and test data set. The training data set are the two months before the data is missing, and the test data set are the two months after the data is missing. The model hyperparameters are tuned using these data sets, and then the missing data points are estimated using the trained model. \n",
    "\n",
    "A value correction function is introduced to avoid negative solar radiation values (there were some slightly negative values observed after the regression, up to -2). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = pd.DataFrame({'Temp': dict_dwd['tu'].TT_10.resample('H').mean()['2021-05-01':'2021-06-29'].values, \n",
    "    'Hour': dict_dwd['tu'].TT_10.resample('H').mean()['2021-05-01':'2021-06-29'].index.hour.values,\n",
    "    'SZA': SZA['2021-05-01':'2021-06-29'].SZA.values})\n",
    "y_train = dict_dwd['sd'].GS_10.resample('H').mean()['2021-05-01':'2021-06-29'].values\n",
    "\n",
    "x_test = pd.DataFrame({'Temp': dict_dwd['tu'].TT_10.resample('H').mean()['2021-07-12':'2021-08-30'].values, \n",
    "    'Hour': dict_dwd['tu'].TT_10.resample('H').mean()['2021-07-12':'2021-08-30'].index.hour.values,\n",
    "    'SZA': SZA['2021-07-12':'2021-08-30'].SZA.values})\n",
    "y_test = dict_dwd['sd'].GS_10.resample('H').mean()['2021-07-12':'2021-08-30'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root mean squared error: 7.39\n",
      "R_squared score: 0.77\n"
     ]
    }
   ],
   "source": [
    "#KernelRidge regression with sklearn\n",
    "regr = make_pipeline(StandardScaler(), kernel_ridge.KernelRidge(kernel='rbf', alpha=0.9, gamma=0.3))\n",
    "regr.fit(x_train, y_train)\n",
    "regr_y_pred = regr.predict(x_test)\n",
    "print(\"Root mean squared error: %.2f\" % mean_squared_error(y_test, regr_y_pred)**(0.5))\n",
    "print(\"R_squared score: %.2f\" % regr.score(x_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1ab5df5a580>"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, regr_y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='MESS_DATUM'>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Real data prediction\n",
    "x_test_missingdata = pd.DataFrame({'Temp': dict_dwd['tu'].TT_10.resample('H').mean()['2021-06-30':'2021-07-11'].values, \n",
    "    'Hour': dict_dwd['tu'].TT_10.resample('H').mean()['2021-06-30':'2021-07-11'].index.hour.values,\n",
    "    'SZA': SZA['2021-06-30':'2021-07-11'].SZA.values})\n",
    "regr_y_pred_missingdata = regr.predict(x_test_missingdata)\n",
    "new_GS_data = dict_dwd['sd'].GS_10.resample('H').mean()\n",
    "new_GS_data['2021-06-30':'2021-07-11'] = regr_y_pred_missingdata\n",
    "new_GS_data['2021-06-30':'2021-07-11'][new_GS_data['2021-06-30':'2021-07-11'] <0 ] = 0\n",
    "#Plot missing data\n",
    "new_GS_data['2021-07'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Repeat with diffuse radiation\n",
    "x_train = pd.DataFrame({'Temp': dict_dwd['tu'].TT_10.resample('H').mean()['2021-05-01':'2021-06-29'].values, \n",
    "    'Hour': dict_dwd['tu'].TT_10.resample('H').mean()['2021-05-01':'2021-06-29'].index.hour.values,\n",
    "    'GS': new_GS_data['2021-05-01':'2021-06-29'].values,\n",
    "    'SZA': SZA['2021-05-01':'2021-06-29'].SZA.values})\n",
    "y_train = dict_dwd['sd'].DS_10.resample('H').mean()['2021-05-01':'2021-06-29'].values\n",
    "\n",
    "x_test = pd.DataFrame({'Temp': dict_dwd['tu'].TT_10.resample('H').mean()['2021-07-27':'2021-08-30'].values, \n",
    "    'Hour': dict_dwd['tu'].TT_10.resample('H').mean()['2021-07-27':'2021-08-30'].index.hour.values,\n",
    "    'GS': new_GS_data['2021-07-27':'2021-08-30'].values,\n",
    "    'SZA': SZA['2021-07-27':'2021-08-30'].SZA.values})\n",
    "y_test = dict_dwd['sd'].DS_10.resample('H').mean()['2021-07-27':'2021-08-30'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root mean squared error: 2.51\n",
      "R_squared score: 0.85\n"
     ]
    }
   ],
   "source": [
    "#KernelRidge regression with sklearn\n",
    "regr = make_pipeline(StandardScaler(), kernel_ridge.KernelRidge(kernel='rbf', alpha=0.1, gamma=0.5))\n",
    "regr.fit(x_train, y_train)\n",
    "regr_y_pred = regr.predict(x_test)\n",
    "print(\"Root mean squared error: %.2f\" % mean_squared_error(y_test, regr_y_pred)**(0.5))\n",
    "print(\"R_squared score: %.2f\" % regr.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1ab62966be0>"
      ]
     },
     "execution_count": 452,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Kkrh48opMcS2unukesRY4EX0DwP8BsJGIXiGizwL4cyI6TUQ/BrALwP6Mx8kwSnwLdTkmogpIF0OcVx9IVVEq0w5DTO+hE4Vyr+Tlr2YwFsZx8qp/IrNQZaTtMp9XTHKJSDo5qYpV2SbN95rXv4l+gTMxGSvkGaER5YMmAPftGMUj41usXKvbfSBn56rKlYXOisPG9ZN+rxy1kz2xiTw24USe3sV2so2Na5eI8JVPb7UmFrrWpC2rU5ZMFKREhBUhMrVsVZ/tUNnDumvWRN5jnv8meo3EiTwMo0M3IjRUwqiql2Eza1LXmrRpdca5hnwLPEvLVvX91eqNViy96vou1ZNxFRZwxgpRERpB4R0a9CDEakszE8tRRxiz9LXqRoLYjBgxETqbUSnB72tA4X/Xub5r9WRchAWcsYLKCt61aaTt9WDzhGqtjn0z85g6tICpOzYra3P7Na9lwjh1aKHt+Mf2bjMWMR2Xh641adPqVAmgChuWbXiiNPGzh6/PlQSzhwWcsYLKCtaJEKnVG9g/M49nTpzHj86/KbWy0yzlo9B1eehakzatTpUAXusNdHQRSnqNMKrvK+hvX7yypHV9riSYPSzgjDVkERr7Fe2+wggAL/7yjY7X/aW5rjVq6krQdXnoWpM2rc6oglpZWbaqiXJFCLw0vQeAulKj7PrdjtrpN1jAmUwxdQPIuFCr47G92yIjMsLHm5xb53WT6oSP3rnFmtUZJYBZWLY6Kwi2rIsDhxEymRIXCqeDH3YW9lWrlvImYWppQhB7sWZ4L95TL8BhhIx1goJ6XdkD0WqhqKBFFrbWhgY9vNNYxmJjRXrOYPlXoH1pHlfeNXy8DjKXB7C6eRfnT+/FGiVsXbsFW+BMIuIs6zir7aHZ0x2VA8teCXfdUsGxMxe1xcNWiOKDT5+SRlwMN8/pb5QOD3o4cPtqxMxNk4elxbMIaPmLTSla6nnRxtOvqCxwFnAmESrXQ5C4TMGwOOzaNIJjZy6iWqu36n/o1i9JuvSfnavi4RcWpK4YFV6JcPDura2mEWGSZhoWzX1RtPH0MyoBT9rQgelzdDYKl4WQ1q/2mxP4ESr37RjF5XeW8OTx8y1BDGcZxtW+TtJsYXauiolnTxmJNwA0lgUOHjlrvTphng0jXBgP0wn7wJlEmEaXBB/8cNz1k8fPa703yupLkkBz8MhZNJaTrUAv1OqR/uIkroeipZ6rrlut1bkvaEFgAWcSodr8i+JCra5d+lX23iiSJNDEnTO8oSo7ryzML2k9lCxTz5NMKKrxEK7WIucKg/nCLhQmEeE2Z0NlD8ODHgjqOtU3DJUTW5NxIpbEnRF3TpV4eyWKPG9S10NWDSOStmSTjUc2qbFbJT/YAmcSo0oykW1+eSXC5XeWtFqehdERsaA7w98EDQqLbJwTuzdi4tlTRm6UdWtL+PKnojfxkrpCsgrhSxruKBuPym3GFQbzgQWcsY4s9vutt5daoXgmlIhw1y1XJ4ooV4D/M+y+CBfMCo/TJAplRUPr07hCskg9TzKhhD9nv0iYKvqIKwzmA7tQmEwY317Bi5O34qXpPRhcuwYNHeWTsCwEZn74MmbnqlquAJWPvVZvSN0G49srmPvSbTg3vQcVDRHK0xWSFJW4ql6P+pyLdm/9Dgs4YwU/NPCmycPYOX20TSjjWp5Vhsq4f8eoUkAbywJffP7HePDpU7G+5ahrxYmvTJxk6LhCgvsDlaFyrrHTpqIb53Ip0r31O+xCYVITF3WhcinIEl42TB6WXkOVeu9fb3auGnktnyjxDbt+VM0M8nKFJMXUtx7ncinSvfU7LOBMauIstm4U9vcnjLjwxjjxDYqTTq0VV1LNTUSXO+m4Aws4kxodiw3QswCHyl6izU5/wvAtetnGpFci7No0op2EEjdu03hvV8SeO+m4Aws4kxrdGtI6YjV1x2ZMPHMq0aZneMIIn2d5RWDmBy+3XtNJQomrx60bnmez2XHWcEVCd2ABZ1ITZbHpWp1RVQV3bRrBcyersRmcwQnj4JGzHZPAiljtLBMkTflXk/A8ldg/+PQpAMUU8aKNiemEBZxJjW7rr2qtjv0z89g3M99WZTBsnV5abKDsldoaFI+9f31HXHlQoMNLfBtdeeIw8RWrrqFTd5xhVLCAM1aQWWw7p492WJ2+5AZdCDquCFkzhyjL3qTYVtLNORNfcdR4kqwCXPGnM9nCAl5gXH9I4yxbX7iSZArGLfFl4uqVCBCItNxNMPEVx0XHmKwCXPKnM9nCAl5QXHxIwxPO0KAXm6IeVWMjTdhalFvH5qSo6yv2j1F1/jG5115s5cYkgwW8oLj2kMomHG+A4JUosliUL6LdCluLKsCV9WpHVqsFML/XotUNZ/KDBbyguPaQyiacxorAUNnDumvWoFqrKxsW226MAJitYLq52rERoseJNowPC3hByeohzcrSVE0sb9YbmD9wW9u1VeVebQqryQqm26udtCF6nGjD+HAxq4KSRdW3uGp+UQWpos65c/pobOcaAK20+rJX0up5maYno8kKxrXVDheUYnzYAi8oWWTDxQmiqbUrqxUShADs2jSiNYaHX1jouNc0wmqygslitZO1T50TbRgAICHZEW87gOhrAD4B4HUhxAebr60HMANgA4BzAD4thLgUd7GxsTFx4sSJlEPuPboVLnjT5GGppUxQi5isYqCPqrh/kLJXarMOVWOQve9ab0AaxRI1Jh9VISqZpSrtIDRAeNe1a1BbbBh/JybX9o93OVyUyR4iOimEGAu/ruNC+R8APhZ6bRLA3wshPgDg75u/MwlQuTUemj1t7M6II6qwfxJrV8cSDrs8dK3aemMZbzeWpfW5F68sxX4eJm4GWX9P0GpGqEkPSR8T10/SfpUMA2gIuBDiewDeCL38SQBfb/7/1wGM2x1W/6B62J86ft76Q71r0wjC7YZ9v7pp15a4vwUJCv3E7o2rCTUa1BsruOuWyqqgBri0KO+u4+P75ffPzAMAHtu7DS9O3hrb/9HvILTumjUdoY8mjXtt1EjhJsHdJcn+TxFIuon5XiHEqwDQ/Pke1YFE9AARnSCiExcvXkx4ud5F9bDb7vw9O1fFcyerbecloNVvMsmmqW4Hmw6hNyg0eOzMRay7pnOrxi8EFX7QbFi0JgIse/BNJkPXNlB7EZdXQZlHoQghnhBCjAkhxkZGRuLf0GeYbJSleahllp7AqkDOzlXx8AsLbX8fKnsdLoewWAFocz0MD3rwBtqt6/AkMHVowahUbLVWjy0EFdcT03Ty0xVg1YO/a9OI9mSYZOXD2MXlVVBSAX+NiK4HgObP1+0Nqb+QWbEqB0Oah1olgtVaHRPPnurYLLz8zlLb7yqxAtByPcx96TYcvGdrh98ZWN3w3DB5OFGzhsG1aitftyemyeSnuxpRPfjHzlyU+t8BdFjr3CQ4f1xeBSUV8EMAPtP8/88A+Jad4fQfss22+3aMWn+oVeJfInmqe2NFtAmjqZUiAPy/N9/Gvpl57J+Z164MKOPyleVIv3nwQbNh0epugEY9+EGfuh8xo5oAOaY7X1xeBcXGgRPRNwB8FMC7iegVAAcATAN4mog+C+A8gHuyHGSvI4vpDda/loWWmYaeqbL3opokVGv1VvsxldMjKGLh8Dk/Wce8t04n69auwZtvNyCLeg0+aLayFHXirE3ix6MmwLgNViZbXM5sjRVwIcS9ij/9geWxMAGiBCRJirkqMchPbVcRZzmHu+DEdc1JSq3ekBbG8gao7UHrZjswkwff5WV6r+NyCznOxHSAoLVd9gaw2FjpOEandoc/Kfjn2z8zj+vKHkoDhOUEPSjTdMExReXqkZVm7VaWosmDzwWoio2rma0s4AUnbG3LxNtHR0DD56vVG/AGCKUS4UpE2dcgfuZm0i44flXCobKHy1eW2oRZNplEuXpWBHKtk6774Lu8TGeKCwt4wTFxSwwNei2ftcoaVJV9LZFeck1UGvvE7o3YPzMf6fOuhMYl8+X749R19RS5TrqPy8t0priwgBccXbeEVyK89fZSKxxQ5RePiqmOg4BIi3F8ewX7mtmPMs5N75G+R5XeHiaqcFaaKJdu4eoynSkuLOAFR9ctsW7tmo4Yaz9bEbgqiKrzlYgiRZwA3LdjNFaAKhFFsdIQ15KMADw0exrHzlxMZeG6UljKlXEy2cL1wAuOTrr6/TtG8aYiQSacrahKHLn3wzcqE4oqQ2U8tncbHhnfkmi8aXy9wezPg0fO4t4P3yhNdBJA6voxrqRUuzJOJnvYAu8CaaylsO+07A2gvrQCIVat5ns/fCMeGd+CY2cuavmIo3yxcbHnScYbd56oz0YWLhmu5xJEVT9G9x5c6UPqyjiZ7ImtB26TfqwHblob2vTcvvhdJ4noCEIAXpL4oGXn6taSPK4O94DCrRPn7gkSd99Bouql656jG7gyTsYeqnrgbIFnTBJrSUdMVeGAA7QaWhcmKt64m019g6giYvyNWJVILwsRm0XqYxJnHRWrHfxOhgY9CLHa7zMP/zPHlDM+7APPGNMMPF3/pkr8/sW1nrEPeurQgnKSyZKkiT9+vZC40EdT37vKf79r00jbd3JpsYFaPVmzBxtwASzGhy3wjNG1loId28PILHaV+NXqDQyVPVzrDaC22GhZi/tn5nHwyFls+JdlHP/VJSyL1djvHb89rKwQmHWat26ETRBfqMa3V1oNG2SE4811iCo3EGXtd9v/zDHljA8LeMboZODFNQcGOsU0Svxq9UYr27HWbAsGrFqLwfcsC4EXfxluttR+jSyRfTYySkRYEaJDqEz7eOq4pmSx2lEThY///ZjsJaTd3GbBZljAM0bHWtLJtpT5Yb0BUjZHEKGfSZAtyW1udoY/G9lGrL/h6x/nryQmdm80Sk9P4+fXWSn434/uNfLad2B6C45CKQBxndrLXgl33VLBcyer7REbJZIm8NhgeNDD3Jdua3tNN6ImjcirUutV1wXkk2P4PJffWZJ+Tkk73Afxx6JygcmusXP6qNHqgelvOAolB3SFLMrCq0T4YRvLotUv0qaIl70S9vzu9R11VXQiatJaljLXwM7po5G1tIGrIn7wyFmc+PUbbZNdlPWs4+cPrxRUUSgqV4vsGlxelrEBC3hGmAiZyhUQtDBVIlSt1TE86En/psvOm9fj3D+t+sdLRKg3lvHU8fNtvvOoGidB0ckiySRK7GSfc3Dscej6+W03eOBQQMYGLOAZYSJk/u8Pv7DQioG+Zs1AhyUpo0SE2mJy63vnzevx1Oc+0iGEJo6168pXJxAdy9I0pjpK7FTNmnWwHXpn4pM3LS/LtU8YGSzgGZFkifx2oNZ3rd7QsiSXhcBQ2UvsQjn3T6vjSdNNJxiOrRJbAeDmL3wby0K0ImQAtDVTVq1SosROJ0LEZ3jQw+DaNZmJoEl4n8mxvOHJqGABzwjTJXJSS7IyVMbilaX4AxX4E0oa32twBRAVGqjTI1O2SokSO5V7KThJAKuCf+D2zbGCl9bSNQnv0z1WtZoLV5pk+g/OxMwI02y5JALqny+NC8WfUNL4XgeIWpmIwY7uSZF9FuEu775oqT7n+3aMGnd6l2XBTjx7Ctse/g5umjyMndNHc6n4F1XDnasQ9jdsgWeEyq+twrQdWUXDCtU5lz+hTOzeGLlRGYUvJMBVq3J8eyU2PFKFyWRiMytRFenju6fycl1E/dvgKoT9DQt4RszOVTF1aKHNN12rNyIjUSaePdVRTXAAwHWDHmqL8k2+2bkqLr+TzIUiAuMY317pGK8JMiFJkyofJsq1YSsrUWcVlIdgxmWscuhh/8ICngFRiR8yAfDFSVYKdgXA4No1bUk1wbopYV+vCWE3x9Qdm7VS21WExTpKePxxD2tEoXRrE093wum2YMZ1I+LQw/6FBTwD4iI6wiF1caJZrdVx0+Rh3DBUxq5NI22hhUnF2ytRh6UbdEf4MeG6dbcBdFQHVJ3PtNBUtxoY6NZmyUMw/fvkzvZMEBbwDIiz0IICoBu+52+qmSSpBAnXCW8sC+wL1BUJCyEB+FfXXYuJ3Rvxp0/PS2uMh5GJvQ33RreyFmUZl2+9vdRWb6abgilzG/kp+xwPzgAs4ImJ8slGLcXDAmAqQkktbpUAB90RAKSuCh3xBqIbF6cJz4uKLd85fdSqiIUnnLwSaFRuo0fv3MK1UpgWLOAJiPPJqpbiw4NeRyxyko0+2wSbN8hcFTrEZRGm8WFHuTayjgwxXUHYEnzue8nowHHgCYh6uIDVh/6uWyotn3CJCPfvGMWB2zfj4JGzbTHFOl3nu0G1VjdeDVDzv7g467jPK4642PJudA/SwWa3eC52xejAFngC4h6u2bkqnjtZbfmEl4XAzA9fxswPXm75U4NL4qhSpN1E5SkZlviCvQHCwXu2tlUhDFYv3LVpBMfOXMSFppjJMBGjuNjyIgibTauZi10xOrAFngDVQ+S/rkoICTdfCD7cL07eisf3buuwxr1SdN/HrPFT0A/es7UtszEs3mHL88nj51u/q0giRnGffZ7YtJq57yWjA1vgCYirJGfywAaPjerJmId1XiJqc40EBdvvjnNDsxaLaex4UjGSffaE1UnD9oamKTatZu57yejAAp6AuIfLZGMy/HDLNs1O/PqNxOGDaVgRQiupJgnXeskWf+HY8mAiU95V+kxLxMbBfS+ZOFjAExL1cO3aNNIhuF6JAIE2Nwo1jw0TjGQYXFvC5SvJMiPTIrMcU5WdRXsZWZnYmjQelrUlyzNSg61mptuwgFvG38AMijcB2PtvbgSANmEXAJ47WcXY+9crW5IlEe+hsod116xRNgrWReaWSLpZKEv5T9uOzbR5RFTPTFtCy1Yz001SCTgRnQPwGwDLAJZkTTf7DVVd7yePn0eJSCpiwbrOaSxcn1q9gU9svR6PjK+2ZJudq+KLz/8Yi4GGEUGi6qmERVTlHip7A3i7sdIxcfmVE1WuljTt2OJ8zqoJQdYzkxskMC5iIwpllxBiG4v3KlEWqqquSLCus61wuCePn8dDs1dF6af/5ePYefP6juPKXglDMT01641l7JuZx87po9i1aUQaHfHonb+Lx/Zua8Vq+5OVX/dEFcMddNOYRnHERWqoJoRvfP/lVHHpDFMUOIzQMknD2XwBsRkO943vv9z2+1Of+wgeb4psMAFHtyFEtVbHcyeruOuWirRZgp+FWvZKrcnKt25Vwh/c4DMNEQwm+MgSiqIaIcgoQiw5w5iQ1gcuAHyHiASA/y6EeCJ8ABE9AOABABgdHU15OTVFafqqW9FORrVWx+N7txm9f13EJqducSmTMMV6YxnHzlxU1uNQWb3HzlyMLcRkEsUR/r4f27tNuxmyqspiEWLJGcaEtAK+UwhxgYjeA+C7RHRGCPG94AFNUX8CAMbGxjKJhCtC09dgje6BhLk3/tsevXNLWycfFWWvhC9/aouyTnS4vKsK00knylKNcoOoNvjCXeqvWTOQuD440F5N0BugjmqCd91SafOB+69zkgzjGqlcKEKIC82frwP4JoAP2RiUKWlrbaQlmIkIqCv/xSGA1obd4Fr53Foi6nAX3PvhG6XHql4PE3ZFDA96GCqr/eJRlqqpGyScxXlpsYF3llbw2N5tbb0vg6i+76lDCx3nAq1G5QQ/s0fGt0S6XhjGFRJb4ES0DsCAEOI3zf+/DcB/tjYyA/Iu/GMjcsTHb96gmgNWhMBL03vaXht7/3p880fVDlfKsTMXMTtXNRamwbVrWtaoaWKKaTJLkvohqu9V1g6usSyw7po1mD9wW9vrHO7H9AJpXCjvBfBNWl2mrwHwt0KI/2llVIZkWfhndq7a5s4YKnuYuqO9JKztNHed+iE6bdV0XUkyl8S+mXkMlT383uh1OP6rS1gWAiUi3HVLtPCZJrMkmXxNS/Dy5iTTqyQWcCHErwBstTiWxNhOYfaZnat2NBqu1Rv406fnAVwVK9PWY2mY2L2xQ3CjrlxvLOPhFxYiBVW1gqjVG3jxl2+0fverKgYTj2SYWLdJJl/V932tNyDdN+DNSaZX6YkwwrhwsiTMzlXx4NOdXeKBVR/3vpl53PyFb2PD5OGuibePqcvm0mIjska1iTXbWBZ4+IUFk+FGkqTqnur7PnD7Zq7gx/QVPZNKb9OnOTtXxcQz8siOIN0WbkDdmdyEcPan6QoiLjrGhKT1Q6K+7yKEkzJMN+gZAbfJ1KGFjtrdppS9Et43fC1+/vplS6NaZVmIyNR3k/P4/vE8JqIgNidf3pxk+omecKHYRhbNYMqjd27B4hV57RHd+GwVUXI76A1gOCY13seP9ohqRiyj7A1g5/TRttZwDMN0HxbwDFi3toQHnz6l9C3rWLxrE3biWWyswMSgvlCrK/tylr2BjqSkAQBLK8JK30eGYdLBAi5B14JVcfnKcqRI61jgjWWBx/duS2Stm6wgbhgqSzcF798xivXrrmlLSioRoby21LGxy4WgGCYfSHTR/zk2NiZOnDjRtevpIKuhAqAjfNAWZa+kFUEyPOhh7ku3dYQM+ue4Zs1AalePN0B417VrUFtsdNTKTlLPpTJUdmLzsCh1cxhGFyI6Kav42tcW+EOzp7F/Zr7DHQAAB++2H+Luh7vp+Jz9eVUVMjd1R2fIXBzr1pZaaeVDZQ+g1YiSsCskSWap35ey6G4VWQPmoo6VYeLoyyiU2bkqpg4tSC1Y3x3w4uSt2Dczb+2aJaK2Cn77Z+YjNyPfDIwtLmROJ467MlRuu/7O6aMd9+/fu2nmok63naKQJHWfYYpK3wm4jnvAF7ABSl6YKkywsNT49krs5BBOmffboxGhzeXx4uStsfckS2ZRiXS1VjeOC1cdWcQU9rzr5jCMTfrOhaLjHhggwuxcFQkDQTq4f8doq72ZT5QbxRfc8HK/Vm+0uTz2z8xjw+RhHDxytq3JwlDZw/CgF5mVGpVebiLelaGyVredomBaLZFhikzfWeA67oZlIfDgM6ewLA/jNmLnzes7xBtQ1+AOFsvaOX00crLxZdbvlGNSPkCnBrhvifs/w66SoGWfRS2aLMiqbg7D5EFfCfjsXFU7i3HZgu/kA+9Zh6c+9xHp33RSyE2W9aZ+3OD1VZPaihA4FyhdGxe94UJkR9LUfYYpIn0RRhgsvZoVQX+5rORsEnZOHzUaMwF4aXqPcZic6jrhjU+GYfJBFUbYswKuUy/bJo9LejKmxTQee3jQw4HbN3e8x7//ikLMVbHmd91SwbEzF3OxVDlWm2GuohLwnnShmNTLtsHwoJeJuISX+34Uiqoa4FtvL+HhFxY6BD/oK5c1eJC5FXZtGmnrG9nNPqNF6HHKMC7Qkxa4qeshDXlZqtse/k7iTEwd10iebhV26TBMO31lgXcrptdvMZbEUk3rIngzRRq9zufT7Xjp4OfhUly5D7t8mDzoCQEPPzxlbwCLDQsxgDGsCIFjZy4aZ/bZcBGoWpENlT28s7QS6TfXiXnOss9oGF1ff1Fjtdnlw+SF84k8stoW3RBvYFVQkliqUencuqhakU3dsbmt3ko4F0k35jlJq7Ok6CRXFTlW28b3yTBJcN4CT1J4yRQidNTY9gVFFZ4YZS3acE/ExTP7P5Mu7bsZLx113wQU3iXB6flMXjgn4GFB6sZmZVi8w3Heppl9ttwTOu3D0rQY61Z7MtXn4cqmZTfdTQwTxCkXisxdkhdBS1dW7jVK+LrpnnAB1z8P18fPuItTFng33CU6hMP3TC1VTudux/XPw/XxM+7ilID3kk+x37uny3zzLrhLVPT798nkg1MulKL4FNP2zOx3uCsOw9jBCQGfnat2NbsyCq9EOHD75ryH4TQcdscwdii8gM/OVTHxzKmuiXeJCPfvGFU2Rzh491ZeKqeEw+4Yxg6F94FPHVpAw1ZfsybeAGHvh25sS4EHViMHTJoiMMngsDuGsUPhLfCkBZtUDJU9HLxnKx4Z32Ic/sfYgcPuGMYOhbbA//Av/8HauWRNFvo5ciDP4kscdscwdii0gP/89cupz6FqYtDPFKH4Uj9Pngxji0ILeFJstTTrVaKiQPgzYxh36CkB501IPTgKhGF6g1SbmET0MSI6S0S/IKJJW4NKNhaweGuiivbgKBCGcYvEAk5EJQD/DcDHAfwOgHuJ6HdsDcwYwcXzdeEoEIbpDdK4UD4E4BdCiF8BABH9HYBPAvipjYGZwtajPhwFwjC9QRoBrwB4OfD7KwA+HD6IiB4A8AAAjI6OpricGrYezeEoEIZxnzQ+8HC3LgCd/WiFEE8IIcaEEGMjIyMpLieHE3AYhulX0ljgrwC4MfD7+wBcSDccfc5N7+nWpRiGYQpJGgv8hwA+QEQ3EdFaAH8E4JCdYTEMwzBxJLbAhRBLRPR5AEcAlAB8TQixYG1kANYQsCSpY7VG5rxhGIbpM1LFgQshvi2E+NdCiJuFEF+2NSifXzy6p0Os19Dq6wzDMP1O4TMxWawZhmHkFL6cLMMwDCOHBZxhGMZRWMAZhmEchQWcYRjGUVjAGYZhHIWEsNswOPJiRBcB/Drh298N4B8tDqeI9Po98v25T6/fY1Hv7/1CiI5aJF0V8DQQ0QkhxFje48iSXr9Hvj/36fV7dO3+2IXCMAzjKCzgDMMwjuKSgD+R9wC6QK/fI9+f+/T6PTp1f874wBmGYZh2XLLAGYZhmAAs4AzDMI7ihIAT0ceI6CwR/YKIJvMej22I6BwRnSaieSI6kfd4bEBEXyOi14noJ4HX1hPRd4no582fw3mOMQ2K+5siomrze5wnon+X5xjTQEQ3EtExIvoZES0Q0Z80X++l71B1j858j4X3gRNRCcD/BfCHWG3j9kMA9wohfprrwCxCROcAjAkhiphAkAgi+n0AbwH4GyHEB5uv/TmAN4QQ082JeFgI8Wd5jjMpivubAvCWEOIv8hybDYjoegDXCyF+RES/BeAkgHEAf4ze+Q5V9/hpOPI9umCBfwjAL4QQvxJCXAHwdwA+mfOYmBiEEN8D8Ebo5U8C+Hrz/7+O1YfFSRT31zMIIV4VQvyo+f+/AfAzABX01neoukdncEHAKwBeDvz+Chz7kDUQAL5DRCeJ6IG8B5Mh7xVCvAqsPjwA3pPzeLLg80T046aLxVn3QhAi2gBgO4Dvo0e/w9A9Ao58jy4IuKwDZrH9PubsFEL8HoCPA/hPzeU54x5/BeBmANsAvArgK7mOxgJE9C4AzwHYJ4T457zHkwWSe3Tme3RBwF8BcGPg9/cBuJDTWDJBCHGh+fN1AN/EqtuoF3mt6Xf0/Y+v5zweqwghXhNCLAshVgD8NRz/HonIw6qwPSWEeL75ck99h7J7dOl7dEHAfwjgA0R0ExGtBfBHAA7lPCZrENG65gYKiGgdgNsA/CT6Xc5yCMBnmv//GQDfynEs1vGFrcmn4PD3SEQE4KsAfiaE+MvAn3rmO1Tdo0vfY+GjUACgGcbzOIASgK8JIb6c74jsQUS/jVWrG1htMv23vXB/RPQNAB/FannO1wAcADAL4GkAowDOA7hHCOHkRqDi/j6K1WW3AHAOwH/0/cWuQUT/FsD/AnAawErz5S9i1UfcK9+h6h7vhSPfoxMCzjAMw3TigguFYRiGkcACzjAM4ygs4AzDMI7CAs4wDOMoLOAMwzCOwgLOMAzjKCzgDMMwjvL/Ab47CisC/JRlAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, regr_y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='MESS_DATUM'>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Real data prediction\n",
    "x_test_missingdata = pd.DataFrame({'Temp': dict_dwd['tu'].TT_10.resample('H').mean()['2021-06-30':'2021-07-26'].values, \n",
    "    'Hour': dict_dwd['tu'].TT_10.resample('H').mean()['2021-06-30':'2021-07-26'].index.hour.values,\n",
    "    'GS': new_GS_data['2021-06-30':'2021-07-26'].values,\n",
    "    'SZA': SZA['2021-06-30':'2021-07-26'].SZA.values})\n",
    "regr_y_pred_missingdata = regr.predict(x_test_missingdata)\n",
    "new_DS_data = dict_dwd['sd'].DS_10.resample('H').mean()\n",
    "new_DS_data['2021-06-30':'2021-07-26'] = regr_y_pred_missingdata\n",
    "new_DS_data['2021-06-30':'2021-07-26'][new_DS_data['2021-06-30':'2021-07-26'] <0 ] = 0\n",
    "#Plot missing data\n",
    "new_DS_data['2021-07':'2021-08'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create new .epw weather file for Weihenstephan (year 2021) with:\n",
    "- DWD measurements\n",
    "- Data estimation (Wet bulb temperature)\n",
    "- Filled data points (Global and diffuse solar radiation)\n",
    "\n",
    "The .epw file is created using the software Elements. The timeseries.csv is edited. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Unnamed: 0.1', 'cloudCover', ' dryBulbTemp_C',\n",
       "       ' elapsedTime_hrs', ' horizInfraredSolarRadFromSky_Wh__m2',\n",
       "       ' pressure_kPa', ' solarBeamNormal_Wh__m2', ' solarDiffuseHoriz_Wh__m2',\n",
       "       ' wetBulbTemp_C', ' windDirection_deg', ' windSpeed_m__s',\n",
       "       ' relativeHumidity_%', ' dewPointTemp_C'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_elements = ('files/timeseries.csv')\n",
    "elements_weihenstephan = pd.read_csv(path_elements, sep=\",\", decimal  ='.')\n",
    "elements_weihenstephan.columns #see variables to modify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wet-bulb temperature estimation\n",
    "T = dict_dwd['tu'].TT_10.resample('H').mean()\n",
    "rh =  dict_dwd['tu'].RF_10.resample('H').mean()\n",
    "T_wb = T * np.arctan(0.152*(rh + 8.3136)**(1/2)) + np.arctan(T+rh) + \\\n",
    "         0.00391838*(rh)**(3/2) * np.arctan(0.0231*rh) - 4.686 - np.arctan(rh-1.6763) \n",
    "         \n",
    "# Estimation of solar zenith angle to calculate normal solar beam\n",
    "# Estimation source -> http://www.diva-portal.org/smash/get/diva2:1329374/FULLTEXT01.pdf\n",
    "latitude = np.radians(48.4024)\n",
    "h = (dict_dwd['tu'].TT_10.resample('H').mean().index.hour -12)/12\n",
    "angle_declination = 23.45*np.sin(np.radians(360*(284+(dict_dwd['tu'].resample('H').mean().index.day_of_year-1))/365))\n",
    "solar_zenith_angle = np.sin(latitude)*np.sin(np.radians(angle_declination)) + \\\n",
    "    np.cos(latitude)*np.cos(np.radians(angle_declination))*np.cos(h)\n",
    "\n",
    "# Calculation Direct Solar Beam Normal\n",
    "DNI = ((new_GS_data*10000/3600)- \\\n",
    "         (new_DS_data*10000/3600)) / solar_zenith_angle\n",
    "DNI[DNI<0] = 0\n",
    "\n",
    "# Could cover - fill the gaps \n",
    "CloudCover = dict_dwd['tu'].TT_10.resample('H').mean()\n",
    "CloudCover[dict_dwd['n'].loc[:,' V_N'].resample('H').mean().index] =  np.floor(dict_dwd['n'].loc[:,' V_N'].resample('H').mean().interpolate(method='linear', axis=0)*10/8)\n",
    "CloudCover.loc['2021-12-31'] = CloudCover.loc['2021-12-30'].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace the values with measurements from DWD in Weihenstephan\n",
    "# Cloud cover - conversion from eights to tenths\n",
    "elements_weihenstephan.cloudCover = CloudCover.values\n",
    "\n",
    "# Dry-bulb temperature\n",
    "elements_weihenstephan.loc[:,' dryBulbTemp_C'] = np.round(dict_dwd['tu'].TT_10.resample('H').mean().values, decimals=2)\n",
    "\n",
    "# Longwave radiation - converstion from J/cm2 to Wh/m2\n",
    "elements_weihenstephan.loc[:,' horizInfraredSolarRadFromSky_Wh__m2'] = np.round(dict_dwd['sd'].LS_10.resample('H').mean().values*10000/3600, decimals=2)\n",
    "\n",
    "# Air pressure\n",
    "elements_weihenstephan.loc[:,' pressure_kPa'] = np.round(dict_dwd['tu'].PP_10.resample('H').mean().values/10, decimals=2)\n",
    "\n",
    "# Direct normal radiation\n",
    "elements_weihenstephan.loc[:,' solarBeamNormal_Wh__m2'] = np.round(DNI.values, decimals=2)\n",
    "\n",
    "# Diffuse radiation - converstion from J/cm2 to Wh/m2\n",
    "elements_weihenstephan.loc[:,' solarDiffuseHoriz_Wh__m2'] = np.round(new_DS_data.values*10000/3600, decimals=2)\n",
    "\n",
    "# Wet-bulb temperature\n",
    "elements_weihenstephan.loc[:,' wetBulbTemp_C'] = np.round(T_wb.values, decimals=2)\n",
    "\n",
    "# Wind direction\n",
    "elements_weihenstephan.loc[:,' windDirection_deg'] = np.floor(dict_dwd['ff'].DD_10.resample('H').mean().values)\n",
    "\n",
    "# Wind speed\n",
    "elements_weihenstephan.loc[:,' windSpeed_m__s'] = np.round(dict_dwd['ff'].FF_10.resample('H').mean().values, decimals=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 579,
   "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>Unnamed: 0</th>\n",
       "      <th>cloudCover</th>\n",
       "      <th>dryBulbTemp_C</th>\n",
       "      <th>elapsedTime_hrs</th>\n",
       "      <th>horizInfraredSolarRadFromSky_Wh__m2</th>\n",
       "      <th>pressure_kPa</th>\n",
       "      <th>solarBeamNormal_Wh__m2</th>\n",
       "      <th>solarDiffuseHoriz_Wh__m2</th>\n",
       "      <th>wetBulbTemp_C</th>\n",
       "      <th>windDirection_deg</th>\n",
       "      <th>windSpeed_m__s</th>\n",
       "      <th>relativeHumidity_%</th>\n",
       "      <th>dewPointTemp_C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-2.15</td>\n",
       "      <td>0</td>\n",
       "      <td>39.21</td>\n",
       "      <td>94.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2.82</td>\n",
       "      <td>158.0</td>\n",
       "      <td>1.12</td>\n",
       "      <td>93.12</td>\n",
       "      <td>-3.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-3.10</td>\n",
       "      <td>1</td>\n",
       "      <td>39.54</td>\n",
       "      <td>94.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3.53</td>\n",
       "      <td>151.0</td>\n",
       "      <td>0.82</td>\n",
       "      <td>96.37</td>\n",
       "      <td>-3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-3.28</td>\n",
       "      <td>2</td>\n",
       "      <td>43.80</td>\n",
       "      <td>94.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3.65</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>97.13</td>\n",
       "      <td>-3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-3.07</td>\n",
       "      <td>3</td>\n",
       "      <td>45.93</td>\n",
       "      <td>94.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3.51</td>\n",
       "      <td>241.0</td>\n",
       "      <td>0.62</td>\n",
       "      <td>96.13</td>\n",
       "      <td>-3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-2.97</td>\n",
       "      <td>4</td>\n",
       "      <td>43.10</td>\n",
       "      <td>94.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3.46</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.65</td>\n",
       "      <td>95.45</td>\n",
       "      <td>-3.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-3.55</td>\n",
       "      <td>5</td>\n",
       "      <td>42.22</td>\n",
       "      <td>94.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-4.05</td>\n",
       "      <td>278.0</td>\n",
       "      <td>0.57</td>\n",
       "      <td>95.33</td>\n",
       "      <td>-4.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-3.28</td>\n",
       "      <td>6</td>\n",
       "      <td>46.57</td>\n",
       "      <td>94.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-3.69</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0.88</td>\n",
       "      <td>96.60</td>\n",
       "      <td>-3.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-3.15</td>\n",
       "      <td>7</td>\n",
       "      <td>44.17</td>\n",
       "      <td>95.04</td>\n",
       "      <td>0.70</td>\n",
       "      <td>2.55</td>\n",
       "      <td>-3.70</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>94.72</td>\n",
       "      <td>-3.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-2.65</td>\n",
       "      <td>8</td>\n",
       "      <td>44.77</td>\n",
       "      <td>95.08</td>\n",
       "      <td>1.14</td>\n",
       "      <td>7.55</td>\n",
       "      <td>-3.30</td>\n",
       "      <td>225.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>93.30</td>\n",
       "      <td>-3.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>9</td>\n",
       "      <td>43.06</td>\n",
       "      <td>95.12</td>\n",
       "      <td>42.49</td>\n",
       "      <td>16.20</td>\n",
       "      <td>-2.45</td>\n",
       "      <td>266.0</td>\n",
       "      <td>0.87</td>\n",
       "      <td>84.05</td>\n",
       "      <td>-3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.23</td>\n",
       "      <td>10</td>\n",
       "      <td>43.75</td>\n",
       "      <td>95.16</td>\n",
       "      <td>48.39</td>\n",
       "      <td>18.19</td>\n",
       "      <td>-1.54</td>\n",
       "      <td>160.0</td>\n",
       "      <td>1.23</td>\n",
       "      <td>78.70</td>\n",
       "      <td>-3.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.02</td>\n",
       "      <td>11</td>\n",
       "      <td>42.08</td>\n",
       "      <td>95.13</td>\n",
       "      <td>53.29</td>\n",
       "      <td>17.55</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>198.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>74.87</td>\n",
       "      <td>-2.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.57</td>\n",
       "      <td>12</td>\n",
       "      <td>41.81</td>\n",
       "      <td>95.10</td>\n",
       "      <td>88.07</td>\n",
       "      <td>14.81</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>215.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>67.33</td>\n",
       "      <td>-2.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.03</td>\n",
       "      <td>13</td>\n",
       "      <td>42.13</td>\n",
       "      <td>95.10</td>\n",
       "      <td>57.54</td>\n",
       "      <td>13.94</td>\n",
       "      <td>0.12</td>\n",
       "      <td>276.0</td>\n",
       "      <td>1.28</td>\n",
       "      <td>65.05</td>\n",
       "      <td>-2.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.93</td>\n",
       "      <td>14</td>\n",
       "      <td>42.31</td>\n",
       "      <td>95.12</td>\n",
       "      <td>3.90</td>\n",
       "      <td>8.38</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>266.0</td>\n",
       "      <td>1.42</td>\n",
       "      <td>73.93</td>\n",
       "      <td>-2.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>15</td>\n",
       "      <td>46.34</td>\n",
       "      <td>95.16</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.81</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>325.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>82.67</td>\n",
       "      <td>-1.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16</td>\n",
       "      <td>41.71</td>\n",
       "      <td>95.19</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>330.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>88.45</td>\n",
       "      <td>-1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>17</td>\n",
       "      <td>43.80</td>\n",
       "      <td>95.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.53</td>\n",
       "      <td>288.0</td>\n",
       "      <td>0.68</td>\n",
       "      <td>93.73</td>\n",
       "      <td>-1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>18</td>\n",
       "      <td>49.35</td>\n",
       "      <td>95.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.43</td>\n",
       "      <td>270.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>96.37</td>\n",
       "      <td>-1.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>19</td>\n",
       "      <td>49.91</td>\n",
       "      <td>95.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>285.0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>96.38</td>\n",
       "      <td>-1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>20</td>\n",
       "      <td>50.79</td>\n",
       "      <td>95.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.06</td>\n",
       "      <td>336.0</td>\n",
       "      <td>0.93</td>\n",
       "      <td>95.15</td>\n",
       "      <td>-1.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  cloudCover   dryBulbTemp_C   elapsedTime_hrs  \\\n",
       "0            0         8.0           -2.15                 0   \n",
       "1            1         9.0           -3.10                 1   \n",
       "2            2        10.0           -3.28                 2   \n",
       "3            3         9.0           -3.07                 3   \n",
       "4            4         9.0           -2.97                 4   \n",
       "5            5         9.0           -3.55                 5   \n",
       "6            6         9.0           -3.28                 6   \n",
       "7            7         9.0           -3.15                 7   \n",
       "8            8         9.0           -2.65                 8   \n",
       "9            9         9.0           -1.10                 9   \n",
       "10          10         9.0            0.23                10   \n",
       "11          11         9.0            1.02                11   \n",
       "12          12         9.0            2.57                12   \n",
       "13          13         9.0            3.03                13   \n",
       "14          14         9.0            1.93                14   \n",
       "15          15         9.0            0.83                15   \n",
       "16          16         9.0            0.00                16   \n",
       "17          17         9.0           -0.90                17   \n",
       "18          18         9.0           -1.00                18   \n",
       "19          19         9.0           -0.87                19   \n",
       "20          20         9.0           -0.53                20   \n",
       "\n",
       "     horizInfraredSolarRadFromSky_Wh__m2   pressure_kPa  \\\n",
       "0                                  39.21          94.91   \n",
       "1                                  39.54          94.92   \n",
       "2                                  43.80          94.92   \n",
       "3                                  45.93          94.91   \n",
       "4                                  43.10          94.91   \n",
       "5                                  42.22          94.93   \n",
       "6                                  46.57          94.97   \n",
       "7                                  44.17          95.04   \n",
       "8                                  44.77          95.08   \n",
       "9                                  43.06          95.12   \n",
       "10                                 43.75          95.16   \n",
       "11                                 42.08          95.13   \n",
       "12                                 41.81          95.10   \n",
       "13                                 42.13          95.10   \n",
       "14                                 42.31          95.12   \n",
       "15                                 46.34          95.16   \n",
       "16                                 41.71          95.19   \n",
       "17                                 43.80          95.23   \n",
       "18                                 49.35          95.27   \n",
       "19                                 49.91          95.31   \n",
       "20                                 50.79          95.33   \n",
       "\n",
       "     solarBeamNormal_Wh__m2   solarDiffuseHoriz_Wh__m2   wetBulbTemp_C  \\\n",
       "0                      0.00                       0.00           -2.82   \n",
       "1                      0.00                       0.00           -3.53   \n",
       "2                      0.00                       0.00           -3.65   \n",
       "3                      0.00                       0.00           -3.51   \n",
       "4                      0.00                       0.00           -3.46   \n",
       "5                      0.00                       0.00           -4.05   \n",
       "6                      0.00                       0.05           -3.69   \n",
       "7                      0.70                       2.55           -3.70   \n",
       "8                      1.14                       7.55           -3.30   \n",
       "9                     42.49                      16.20           -2.45   \n",
       "10                    48.39                      18.19           -1.54   \n",
       "11                    53.29                      17.55           -1.07   \n",
       "12                    88.07                      14.81           -0.15   \n",
       "13                    57.54                      13.94            0.12   \n",
       "14                     3.90                       8.38           -0.27   \n",
       "15                     1.09                       1.81           -0.67   \n",
       "16                     0.00                       0.00           -1.05   \n",
       "17                     0.00                       0.00           -1.53   \n",
       "18                     0.00                       0.00           -1.43   \n",
       "19                     0.00                       0.00           -1.29   \n",
       "20                     0.00                       0.00           -1.06   \n",
       "\n",
       "     windDirection_deg   windSpeed_m__s   relativeHumidity_%   dewPointTemp_C  \n",
       "0                158.0             1.12                93.12            -3.12  \n",
       "1                151.0             0.82                96.37            -3.58  \n",
       "2                140.0             0.60                97.13            -3.67  \n",
       "3                241.0             0.62                96.13            -3.58  \n",
       "4                168.0             0.65                95.45            -3.57  \n",
       "5                278.0             0.57                95.33            -4.18  \n",
       "6                173.0             0.88                96.60            -3.77  \n",
       "7                200.0             0.70                94.72            -3.90  \n",
       "8                225.0             0.33                93.30            -3.60  \n",
       "9                266.0             0.87                84.05            -3.50  \n",
       "10               160.0             1.23                78.70            -3.05  \n",
       "11               198.0             0.75                74.87            -2.95  \n",
       "12               215.0             1.00                67.33            -2.88  \n",
       "13               276.0             1.28                65.05            -2.92  \n",
       "14               266.0             1.42                73.93            -2.25  \n",
       "15               325.0             1.20                82.67            -1.78  \n",
       "16               330.0             1.50                88.45            -1.67  \n",
       "17               288.0             0.68                93.73            -1.80  \n",
       "18               270.0             0.70                96.37            -1.52  \n",
       "19               285.0             0.90                96.38            -1.37  \n",
       "20               336.0             0.93                95.15            -1.22  "
      ]
     },
     "execution_count": 579,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elements_weihenstephan.loc[0:20,:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cloudCover                              0\n",
       " dryBulbTemp_C                          0\n",
       " elapsedTime_hrs                        0\n",
       " horizInfraredSolarRadFromSky_Wh__m2    0\n",
       " pressure_kPa                           0\n",
       " solarBeamNormal_Wh__m2                 0\n",
       " solarDiffuseHoriz_Wh__m2               0\n",
       " wetBulbTemp_C                          0\n",
       " windDirection_deg                      0\n",
       " windSpeed_m__s                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Double-check if there are NAs \n",
    "pd.isna(elements_weihenstephan).sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Save to new .csv file\n",
    "elements_weihenstephan.to_csv(path_elements, sep=\",\", decimal  ='.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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