{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pvlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "from scipy.io import loadmat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Polydome experiments have been done in the following order, begining and end dates:\n",
    "\n",
    "| No | Start Date       | End Date         |\n",
    "|----|------------------|------------------|\n",
    "| 1  | 01.06.2017 20:00 | 03.06.2017 17:00 |\n",
    "| 2  | 10.06.2017 16:00 | 12.06.2017 06:00 |\n",
    "| 3  | 16.06.2017 20:00 | 19.06.2017 06:00 |\n",
    "| 4  | 19.06.2017 20:00 | 22.06.2017 06:00 |\n",
    "| 5  | 30.06.2017 20:00 | 03.07.2017 06:00 |\n",
    "| 6  | 07.07.2017 20:00 | 10.07.2017 06:00 |\n",
    "| 7  | 13.07.2017 20:00 | 20.07.2017 06:00 |\n",
    "| 8  | 21.07.2017 20:00 | 24.07.2017 06:00 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the experimental data pertaining to the first experiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Power',\n",
       " 'Setpoint',\n",
       " 'OutsideTemp',\n",
       " 'SupplyTemp',\n",
       " 'InsideTemp',\n",
       " 'LakeTemp',\n",
       " 'SolRad',\n",
       " 'SolRad_DA']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat_exp1 = loadmat('../Data/Luca_experimental_data/Exp1.mat')\n",
    "measured_vals = [*mat_exp1['Exp1'].dtype.fields]\n",
    "measured_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def df_from_mb_struct(mb_mat, field):\n",
    "    df_field = pd.DataFrame({\n",
    "            'timestamp': pd.Series(np.array(mb_mat[field][0,0]['time'][0,0]).flatten()),\n",
    "            'value': pd.Series(np.array(mb_mat[field][0,0]['values'][0,0]).flatten())\n",
    "            })\n",
    "    df_field['timestamp'] = pd.to_datetime(df_field['timestamp'], unit = 's', utc = False) + pd.Timedelta(2, 'hours')\n",
    "    df_field['timestamp'] = pd.DatetimeIndex(df_field['timestamp'], tz = 'Europe/Zurich')\n",
    "    df_field.set_index('timestamp', inplace = True, drop = True)\n",
    "    df_field.columns = [field]\n",
    "    \n",
    "    return df_field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_dfs = {}\n",
    "for var in measured_vals:\n",
    "    dict_dfs[var] = df_from_mb_struct(mat_exp1['Exp1'], var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>SupplyTemp</th>\n",
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       "      <th>SolRad</th>\n",
       "      <th>SolRad_DA</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:00:00+02:00</th>\n",
       "      <td>5995.034483</td>\n",
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       "      <td>22.0</td>\n",
       "      <td>24.5</td>\n",
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       "      <td>122.0</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-01 20:05:00+02:00</th>\n",
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       "      <td>22.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>24.4</td>\n",
       "      <td>24.166667</td>\n",
       "      <td>57.926100</td>\n",
       "      <td>91.5</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-01 20:10:00+02:00</th>\n",
       "      <td>5989.766667</td>\n",
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       "      <td>22.0</td>\n",
       "      <td>15.2</td>\n",
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       "      <td>54.902033</td>\n",
       "      <td>91.5</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-01 20:15:00+02:00</th>\n",
       "      <td>4563.344828</td>\n",
       "      <td>23.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>23.8</td>\n",
       "      <td>24.066667</td>\n",
       "      <td>73.860700</td>\n",
       "      <td>91.5</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-01 20:20:00+02:00</th>\n",
       "      <td>1729.137931</td>\n",
       "      <td>23.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>23.5</td>\n",
       "      <td>23.833333</td>\n",
       "      <td>76.042533</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:35:00+02:00</th>\n",
       "      <td>1732.137931</td>\n",
       "      <td>24.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>16.4</td>\n",
       "      <td>22.3</td>\n",
       "      <td>22.300000</td>\n",
       "      <td>361.247267</td>\n",
       "      <td>570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:40:00+02:00</th>\n",
       "      <td>1727.482759</td>\n",
       "      <td>24.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>22.3</td>\n",
       "      <td>22.300000</td>\n",
       "      <td>596.456167</td>\n",
       "      <td>570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:45:00+02:00</th>\n",
       "      <td>1726.233333</td>\n",
       "      <td>24.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.3</td>\n",
       "      <td>22.333333</td>\n",
       "      <td>550.335400</td>\n",
       "      <td>570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:50:00+02:00</th>\n",
       "      <td>1723.379310</td>\n",
       "      <td>24.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>22.300000</td>\n",
       "      <td>627.393133</td>\n",
       "      <td>570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:55:00+02:00</th>\n",
       "      <td>1728.379310</td>\n",
       "      <td>24.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>22.9</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>564.347267</td>\n",
       "      <td>570.0</td>\n",
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       "<p>540 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Power  Setpoint  OutsideTemp  SupplyTemp  \\\n",
       "timestamp                                                                   \n",
       "2017-06-01 20:00:00+02:00  5995.034483      23.5         22.0        24.5   \n",
       "2017-06-01 20:05:00+02:00  5957.000000      23.5         22.0        15.5   \n",
       "2017-06-01 20:10:00+02:00  5989.766667      23.5         22.0        15.2   \n",
       "2017-06-01 20:15:00+02:00  4563.344828      23.5         22.0        14.9   \n",
       "2017-06-01 20:20:00+02:00  1729.137931      23.5         22.0        18.2   \n",
       "...                                ...       ...          ...         ...   \n",
       "2017-06-03 16:35:00+02:00  1732.137931      24.5         24.0        16.4   \n",
       "2017-06-03 16:40:00+02:00  1727.482759      24.5         24.0        17.6   \n",
       "2017-06-03 16:45:00+02:00  1726.233333      24.5         24.0        18.5   \n",
       "2017-06-03 16:50:00+02:00  1723.379310      24.5         24.0        20.0   \n",
       "2017-06-03 16:55:00+02:00  1728.379310      24.5         24.0        23.3   \n",
       "\n",
       "                           InsideTemp   LakeTemp      SolRad  SolRad_DA  \n",
       "timestamp                                                                \n",
       "2017-06-01 20:00:00+02:00        24.4  24.200000   61.321333      122.0  \n",
       "2017-06-01 20:05:00+02:00        24.4  24.166667   57.926100       91.5  \n",
       "2017-06-01 20:10:00+02:00        24.1  24.066667   54.902033       91.5  \n",
       "2017-06-01 20:15:00+02:00        23.8  24.066667   73.860700       91.5  \n",
       "2017-06-01 20:20:00+02:00        23.5  23.833333   76.042533       91.5  \n",
       "...                               ...        ...         ...        ...  \n",
       "2017-06-03 16:35:00+02:00        22.3  22.300000  361.247267      570.0  \n",
       "2017-06-03 16:40:00+02:00        22.3  22.300000  596.456167      570.0  \n",
       "2017-06-03 16:45:00+02:00        22.3  22.333333  550.335400      570.0  \n",
       "2017-06-03 16:50:00+02:00        22.6  22.300000  627.393133      570.0  \n",
       "2017-06-03 16:55:00+02:00        22.9  22.500000  564.347267      570.0  \n",
       "\n",
       "[540 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = dict_dfs[measured_vals[0]]\n",
    "\n",
    "for idx in range(1, len(measured_vals)):\n",
    "    df = pd.concat([df, dict_dfs[measured_vals[idx]]], axis = 1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Power'] = df['Power'] - 1.67 * 1000 # remove the fan power consumption\n",
    "df['Power'] = np.where(df['Power'] > 6300, 6300, df['Power']) # cap to 1 compressor to remove non-linear effects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take the inside temperature to be the average of the two sensors (`InsideTemp` and `LakeTemp`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['InsideTemp'] = df[['LakeTemp', 'InsideTemp']].mean(axis = 1) # Averaged Inside Temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2017-06-01 20:00:00+02:00</th>\n",
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       "      <th>2017-06-01 20:05:00+02:00</th>\n",
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       "      <th>2017-06-01 20:10:00+02:00</th>\n",
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       "    <tr>\n",
       "      <th>2017-06-01 20:15:00+02:00</th>\n",
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       "      <td>73.860700</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-01 20:20:00+02:00</th>\n",
       "      <td>59.137931</td>\n",
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       "      <td>22.0</td>\n",
       "      <td>18.2</td>\n",
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       "      <th>2017-06-03 16:35:00+02:00</th>\n",
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       "    <tr>\n",
       "      <th>2017-06-03 16:40:00+02:00</th>\n",
       "      <td>57.482759</td>\n",
       "      <td>24.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>22.300000</td>\n",
       "      <td>596.456167</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-03 16:45:00+02:00</th>\n",
       "      <td>56.233333</td>\n",
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       "      <td>24.0</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-03 16:50:00+02:00</th>\n",
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       "      <th>2017-06-03 16:55:00+02:00</th>\n",
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      "text/plain": [
       "                                 Power  Setpoint  OutsideTemp  SupplyTemp  \\\n",
       "timestamp                                                                   \n",
       "2017-06-01 20:00:00+02:00  4325.034483      23.5         22.0        24.5   \n",
       "2017-06-01 20:05:00+02:00  4287.000000      23.5         22.0        15.5   \n",
       "2017-06-01 20:10:00+02:00  4319.766667      23.5         22.0        15.2   \n",
       "2017-06-01 20:15:00+02:00  2893.344828      23.5         22.0        14.9   \n",
       "2017-06-01 20:20:00+02:00    59.137931      23.5         22.0        18.2   \n",
       "...                                ...       ...          ...         ...   \n",
       "2017-06-03 16:35:00+02:00    62.137931      24.5         24.0        16.4   \n",
       "2017-06-03 16:40:00+02:00    57.482759      24.5         24.0        17.6   \n",
       "2017-06-03 16:45:00+02:00    56.233333      24.5         24.0        18.5   \n",
       "2017-06-03 16:50:00+02:00    53.379310      24.5         24.0        20.0   \n",
       "2017-06-03 16:55:00+02:00    58.379310      24.5         24.0        23.3   \n",
       "\n",
       "                           InsideTemp      SolRad  \n",
       "timestamp                                          \n",
       "2017-06-01 20:00:00+02:00   24.300000   61.321333  \n",
       "2017-06-01 20:05:00+02:00   24.283333   57.926100  \n",
       "2017-06-01 20:10:00+02:00   24.083333   54.902033  \n",
       "2017-06-01 20:15:00+02:00   23.933333   73.860700  \n",
       "2017-06-01 20:20:00+02:00   23.666667   76.042533  \n",
       "...                               ...         ...  \n",
       "2017-06-03 16:35:00+02:00   22.300000  361.247267  \n",
       "2017-06-03 16:40:00+02:00   22.300000  596.456167  \n",
       "2017-06-03 16:45:00+02:00   22.316667  550.335400  \n",
       "2017-06-03 16:50:00+02:00   22.450000  627.393133  \n",
       "2017-06-03 16:55:00+02:00   22.700000  564.347267  \n",
       "\n",
       "[540 rows x 6 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns = ['LakeTemp', 'SolRad_DA'], inplace = True) # Drop GHI prediction and the non-averaged inside temp measure\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solar angles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get altitude information for Polydome GPS coordinates:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"Images/polydome_altitude.png\" alt=\"Polydome Altitude Source\" width=\"500\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "latitude = 46.521476\n",
    "longitude = 6.569009\n",
    "altitude = 395"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the solar position from GPS/altitude data:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"Images/solar_angles.png\" alt=\"Diagram of the zenith/azimuth angles definition\" width=\"500\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>apparent_zenith</th>\n",
       "      <th>zenith</th>\n",
       "      <th>apparent_elevation</th>\n",
       "      <th>elevation</th>\n",
       "      <th>azimuth</th>\n",
       "      <th>equation_of_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</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>2017-06-01 20:00:00+02:00</th>\n",
       "      <td>78.691622</td>\n",
       "      <td>78.765922</td>\n",
       "      <td>11.308378</td>\n",
       "      <td>11.234078</td>\n",
       "      <td>290.430819</td>\n",
       "      <td>2.098613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:05:00+02:00</th>\n",
       "      <td>79.489651</td>\n",
       "      <td>79.569259</td>\n",
       "      <td>10.510349</td>\n",
       "      <td>10.430741</td>\n",
       "      <td>291.279501</td>\n",
       "      <td>2.098069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:10:00+02:00</th>\n",
       "      <td>80.282334</td>\n",
       "      <td>80.367965</td>\n",
       "      <td>9.717666</td>\n",
       "      <td>9.632035</td>\n",
       "      <td>292.130503</td>\n",
       "      <td>2.097524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:15:00+02:00</th>\n",
       "      <td>81.069332</td>\n",
       "      <td>81.161848</td>\n",
       "      <td>8.930668</td>\n",
       "      <td>8.838152</td>\n",
       "      <td>292.984123</td>\n",
       "      <td>2.096979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:20:00+02:00</th>\n",
       "      <td>81.850261</td>\n",
       "      <td>81.950719</td>\n",
       "      <td>8.149739</td>\n",
       "      <td>8.049281</td>\n",
       "      <td>293.840653</td>\n",
       "      <td>2.096433</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:35:00+02:00</th>\n",
       "      <td>43.923091</td>\n",
       "      <td>43.937926</td>\n",
       "      <td>46.076909</td>\n",
       "      <td>46.062074</td>\n",
       "      <td>252.722275</td>\n",
       "      <td>1.796947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:40:00+02:00</th>\n",
       "      <td>44.746130</td>\n",
       "      <td>44.761396</td>\n",
       "      <td>45.253870</td>\n",
       "      <td>45.238604</td>\n",
       "      <td>253.882437</td>\n",
       "      <td>1.796365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:45:00+02:00</th>\n",
       "      <td>45.573942</td>\n",
       "      <td>45.589654</td>\n",
       "      <td>44.426058</td>\n",
       "      <td>44.410346</td>\n",
       "      <td>255.018953</td>\n",
       "      <td>1.795782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:50:00+02:00</th>\n",
       "      <td>46.406107</td>\n",
       "      <td>46.422280</td>\n",
       "      <td>43.593893</td>\n",
       "      <td>43.577720</td>\n",
       "      <td>256.133161</td>\n",
       "      <td>1.795200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:55:00+02:00</th>\n",
       "      <td>47.242228</td>\n",
       "      <td>47.258880</td>\n",
       "      <td>42.757772</td>\n",
       "      <td>42.741120</td>\n",
       "      <td>257.226338</td>\n",
       "      <td>1.794617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>540 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           apparent_zenith     zenith  apparent_elevation  \\\n",
       "timestamp                                                                   \n",
       "2017-06-01 20:00:00+02:00        78.691622  78.765922           11.308378   \n",
       "2017-06-01 20:05:00+02:00        79.489651  79.569259           10.510349   \n",
       "2017-06-01 20:10:00+02:00        80.282334  80.367965            9.717666   \n",
       "2017-06-01 20:15:00+02:00        81.069332  81.161848            8.930668   \n",
       "2017-06-01 20:20:00+02:00        81.850261  81.950719            8.149739   \n",
       "...                                    ...        ...                 ...   \n",
       "2017-06-03 16:35:00+02:00        43.923091  43.937926           46.076909   \n",
       "2017-06-03 16:40:00+02:00        44.746130  44.761396           45.253870   \n",
       "2017-06-03 16:45:00+02:00        45.573942  45.589654           44.426058   \n",
       "2017-06-03 16:50:00+02:00        46.406107  46.422280           43.593893   \n",
       "2017-06-03 16:55:00+02:00        47.242228  47.258880           42.757772   \n",
       "\n",
       "                           elevation     azimuth  equation_of_time  \n",
       "timestamp                                                           \n",
       "2017-06-01 20:00:00+02:00  11.234078  290.430819          2.098613  \n",
       "2017-06-01 20:05:00+02:00  10.430741  291.279501          2.098069  \n",
       "2017-06-01 20:10:00+02:00   9.632035  292.130503          2.097524  \n",
       "2017-06-01 20:15:00+02:00   8.838152  292.984123          2.096979  \n",
       "2017-06-01 20:20:00+02:00   8.049281  293.840653          2.096433  \n",
       "...                              ...         ...               ...  \n",
       "2017-06-03 16:35:00+02:00  46.062074  252.722275          1.796947  \n",
       "2017-06-03 16:40:00+02:00  45.238604  253.882437          1.796365  \n",
       "2017-06-03 16:45:00+02:00  44.410346  255.018953          1.795782  \n",
       "2017-06-03 16:50:00+02:00  43.577720  256.133161          1.795200  \n",
       "2017-06-03 16:55:00+02:00  42.741120  257.226338          1.794617  \n",
       "\n",
       "[540 rows x 6 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_solarposition = pvlib.solarposition.get_solarposition(\n",
    "    time = df.index,\n",
    "    latitude = latitude, \n",
    "    longitude = longitude,\n",
    "    temperature = df['OutsideTemp'],\n",
    "    altitude = altitude\n",
    ")\n",
    "df_solarposition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the difference between the real/geometrical zenith and the apparent zenith:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "df_solarposition[['apparent_zenith', 'zenith']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the angle of incidence of the sun on the surface: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"Images/angle_of_incidence.jpg\" alt=\"Angle of incidence\" width=\"500\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "surface_tilt = 0\n",
    "surface_azimuth = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp\n",
       "2017-06-01 20:00:00+02:00    78.691622\n",
       "2017-06-01 20:05:00+02:00    79.489651\n",
       "2017-06-01 20:10:00+02:00    80.282334\n",
       "2017-06-01 20:15:00+02:00    81.069332\n",
       "2017-06-01 20:20:00+02:00    81.850261\n",
       "                               ...    \n",
       "2017-06-03 16:35:00+02:00    43.923091\n",
       "2017-06-03 16:40:00+02:00    44.746130\n",
       "2017-06-03 16:45:00+02:00    45.573942\n",
       "2017-06-03 16:50:00+02:00    46.406107\n",
       "2017-06-03 16:55:00+02:00    47.242228\n",
       "Name: aoi, Length: 540, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aoi = pvlib.irradiance.aoi(\n",
    "    surface_tilt = surface_tilt,\n",
    "    surface_azimuth = surface_azimuth,\n",
    "    solar_zenith = df_solarposition['apparent_zenith'],\n",
    "    solar_azimuth = df_solarposition['azimuth']\n",
    ")\n",
    "df_aoi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solar radiation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>dni</th>\n",
       "      <th>dhi</th>\n",
       "      <th>kt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:00:00+02:00</th>\n",
       "      <td>7.251337</td>\n",
       "      <td>59.908644</td>\n",
       "      <td>0.237114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:05:00+02:00</th>\n",
       "      <td>7.672114</td>\n",
       "      <td>56.537088</td>\n",
       "      <td>0.241022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:10:00+02:00</th>\n",
       "      <td>8.423139</td>\n",
       "      <td>53.492674</td>\n",
       "      <td>0.247180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:15:00+02:00</th>\n",
       "      <td>52.657244</td>\n",
       "      <td>65.770239</td>\n",
       "      <td>0.362136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:20:00+02:00</th>\n",
       "      <td>94.364403</td>\n",
       "      <td>62.829177</td>\n",
       "      <td>0.409096</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 dni        dhi        kt\n",
       "timestamp                                                \n",
       "2017-06-01 20:00:00+02:00   7.251337  59.908644  0.237114\n",
       "2017-06-01 20:05:00+02:00   7.672114  56.537088  0.241022\n",
       "2017-06-01 20:10:00+02:00   8.423139  53.492674  0.247180\n",
       "2017-06-01 20:15:00+02:00  52.657244  65.770239  0.362136\n",
       "2017-06-01 20:20:00+02:00  94.364403  62.829177  0.409096"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_erbs = pvlib.irradiance.erbs(\n",
    "    ghi = df['SolRad'],\n",
    "    zenith = df_solarposition['zenith'],\n",
    "    datetime_or_doy = df.index\n",
    ")\n",
    "df_erbs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "erbs_ghi = df_erbs['dhi'] + df_erbs['dni']*np.cos(np.radians(df_solarposition['zenith']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare `erbs_ghi` with measured ghi to confirm they are the same:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "pd.concat([df['SolRad'], erbs_ghi], axis = 1).plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the in-plane irradiance components:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp\n",
       "2017-06-01 20:00:00+02:00     59.908644\n",
       "2017-06-01 20:05:00+02:00     56.537088\n",
       "2017-06-01 20:10:00+02:00     53.492674\n",
       "2017-06-01 20:15:00+02:00     65.770239\n",
       "2017-06-01 20:20:00+02:00     62.829177\n",
       "                                ...    \n",
       "2017-06-03 16:35:00+02:00    314.462614\n",
       "2017-06-03 16:40:00+02:00    219.485890\n",
       "2017-06-03 16:45:00+02:00    250.653973\n",
       "2017-06-03 16:50:00+02:00    167.328816\n",
       "2017-06-03 16:55:00+02:00    215.275641\n",
       "Name: dhi, Length: 540, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#poa_sky_diffuse\n",
    "df_sky_diffuse = pvlib.irradiance.get_sky_diffuse(\n",
    "    surface_tilt = surface_tilt,\n",
    "    surface_azimuth = surface_azimuth,\n",
    "    solar_zenith = df_solarposition['apparent_zenith'],\n",
    "    solar_azimuth = df_solarposition['azimuth'],\n",
    "    dni = df_erbs['dni'],\n",
    "    ghi = df['SolRad'],\n",
    "    dhi = df_erbs['dhi'],\n",
    ")\n",
    "df_sky_diffuse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp\n",
       "2017-06-01 20:00:00+02:00    0.0\n",
       "2017-06-01 20:05:00+02:00    0.0\n",
       "2017-06-01 20:10:00+02:00    0.0\n",
       "2017-06-01 20:15:00+02:00    0.0\n",
       "2017-06-01 20:20:00+02:00    0.0\n",
       "                            ... \n",
       "2017-06-03 16:35:00+02:00    0.0\n",
       "2017-06-03 16:40:00+02:00    0.0\n",
       "2017-06-03 16:45:00+02:00    0.0\n",
       "2017-06-03 16:50:00+02:00    0.0\n",
       "2017-06-03 16:55:00+02:00    0.0\n",
       "Name: diffuse_ground, Length: 540, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#poa_ground_diffuse\n",
    "df_ground_diffuse = pvlib.irradiance.get_ground_diffuse(\n",
    "    surface_tilt = surface_tilt,\n",
    "    ghi = df['SolRad'],\n",
    "    surface_type = 'urban' # used for gound_diffuse calculation\n",
    ")\n",
    "df_ground_diffuse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "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>poa_global</th>\n",
       "      <th>poa_direct</th>\n",
       "      <th>poa_diffuse</th>\n",
       "      <th>poa_sky_diffuse</th>\n",
       "      <th>poa_ground_diffuse</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</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>2017-06-01 20:00:00+02:00</th>\n",
       "      <td>61.330555</td>\n",
       "      <td>1.421911</td>\n",
       "      <td>59.908644</td>\n",
       "      <td>59.908644</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:05:00+02:00</th>\n",
       "      <td>57.936582</td>\n",
       "      <td>1.399494</td>\n",
       "      <td>56.537088</td>\n",
       "      <td>56.537088</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:10:00+02:00</th>\n",
       "      <td>54.914443</td>\n",
       "      <td>1.421769</td>\n",
       "      <td>53.492674</td>\n",
       "      <td>53.492674</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:15:00+02:00</th>\n",
       "      <td>73.944706</td>\n",
       "      <td>8.174467</td>\n",
       "      <td>65.770239</td>\n",
       "      <td>65.770239</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01 20:20:00+02:00</th>\n",
       "      <td>76.206334</td>\n",
       "      <td>13.377157</td>\n",
       "      <td>62.829177</td>\n",
       "      <td>62.829177</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:35:00+02:00</th>\n",
       "      <td>361.258938</td>\n",
       "      <td>46.796324</td>\n",
       "      <td>314.462614</td>\n",
       "      <td>314.462614</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:40:00+02:00</th>\n",
       "      <td>596.555761</td>\n",
       "      <td>377.069871</td>\n",
       "      <td>219.485890</td>\n",
       "      <td>219.485890</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:45:00+02:00</th>\n",
       "      <td>550.419278</td>\n",
       "      <td>299.765305</td>\n",
       "      <td>250.653973</td>\n",
       "      <td>250.653973</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:50:00+02:00</th>\n",
       "      <td>627.529596</td>\n",
       "      <td>460.200780</td>\n",
       "      <td>167.328816</td>\n",
       "      <td>167.328816</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-03 16:55:00+02:00</th>\n",
       "      <td>564.457032</td>\n",
       "      <td>349.181391</td>\n",
       "      <td>215.275641</td>\n",
       "      <td>215.275641</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>540 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           poa_global  poa_direct  poa_diffuse  \\\n",
       "timestamp                                                        \n",
       "2017-06-01 20:00:00+02:00   61.330555    1.421911    59.908644   \n",
       "2017-06-01 20:05:00+02:00   57.936582    1.399494    56.537088   \n",
       "2017-06-01 20:10:00+02:00   54.914443    1.421769    53.492674   \n",
       "2017-06-01 20:15:00+02:00   73.944706    8.174467    65.770239   \n",
       "2017-06-01 20:20:00+02:00   76.206334   13.377157    62.829177   \n",
       "...                               ...         ...          ...   \n",
       "2017-06-03 16:35:00+02:00  361.258938   46.796324   314.462614   \n",
       "2017-06-03 16:40:00+02:00  596.555761  377.069871   219.485890   \n",
       "2017-06-03 16:45:00+02:00  550.419278  299.765305   250.653973   \n",
       "2017-06-03 16:50:00+02:00  627.529596  460.200780   167.328816   \n",
       "2017-06-03 16:55:00+02:00  564.457032  349.181391   215.275641   \n",
       "\n",
       "                           poa_sky_diffuse  poa_ground_diffuse  \n",
       "timestamp                                                       \n",
       "2017-06-01 20:00:00+02:00        59.908644                 0.0  \n",
       "2017-06-01 20:05:00+02:00        56.537088                 0.0  \n",
       "2017-06-01 20:10:00+02:00        53.492674                 0.0  \n",
       "2017-06-01 20:15:00+02:00        65.770239                 0.0  \n",
       "2017-06-01 20:20:00+02:00        62.829177                 0.0  \n",
       "...                                    ...                 ...  \n",
       "2017-06-03 16:35:00+02:00       314.462614                 0.0  \n",
       "2017-06-03 16:40:00+02:00       219.485890                 0.0  \n",
       "2017-06-03 16:45:00+02:00       250.653973                 0.0  \n",
       "2017-06-03 16:50:00+02:00       167.328816                 0.0  \n",
       "2017-06-03 16:55:00+02:00       215.275641                 0.0  \n",
       "\n",
       "[540 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_poa_components = pvlib.irradiance.poa_components(\n",
    "    aoi = df_aoi,\n",
    "    dni = df_erbs['dni'],\n",
    "    poa_sky_diffuse = df_sky_diffuse,\n",
    "    poa_ground_diffuse = df_ground_diffuse\n",
    ")\n",
    "df_poa_components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp\n",
       "2017-06-01 20:00:00+02:00     61.321333\n",
       "2017-06-01 20:05:00+02:00     57.926100\n",
       "2017-06-01 20:10:00+02:00     54.902033\n",
       "2017-06-01 20:15:00+02:00     73.860700\n",
       "2017-06-01 20:20:00+02:00     76.042533\n",
       "                                ...    \n",
       "2017-06-03 16:35:00+02:00    361.247267\n",
       "2017-06-03 16:40:00+02:00    596.456167\n",
       "2017-06-03 16:45:00+02:00    550.335400\n",
       "2017-06-03 16:50:00+02:00    627.393133\n",
       "2017-06-03 16:55:00+02:00    564.347267\n",
       "Name: SolRad, Length: 540, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SolRad']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print(df_sky_diffuse.equals(df_poa_components['poa_sky_diffuse']))\n",
    "print(df_ground_diffuse.equals(df_poa_components['poa_ground_diffuse']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "df_poa_components.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compile the WDB vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wdb = df.copy()\n",
    "df_wdb['time'] = (df_wdb.index - df_wdb.index[0]).total_seconds()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the solar angles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wdb['zenith'] = df_solarposition['apparent_zenith'] # apparent_zenith takes into account refraction due to atmosphere\n",
    "df_wdb['azimuth'] = df_solarposition['azimuth']\n",
    "df_wdb['aoi'] = df_aoi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the estimated radiation values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wdb['dni'] = df_erbs['dni']\n",
    "df_wdb['dhi'] = df_erbs['dhi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wdb['poa_direct'] = df_poa_components['poa_direct']\n",
    "df_wdb['poa_diffuse'] = df_poa_components['poa_diffuse']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Radiative temperature of the sky\n",
    "df_wdb['Tsky_rad'] = df_wdb['OutsideTemp'] - 6 # from email with Manuel\n",
    "\n",
    "df_wdb['relative_humidity'] = 50 # relative humidity. placeholder\n",
    "df_wdb['precipitation'] = -9999 # placeholder\n",
    "df_wdb['cloud_index'] = 0.5 # from email with Manuel, for the moment assumed to not be used by the model\n",
    "df_wdb['pressure'] = 96300 # placeholder, taken as mean of PUY station over the last year\n",
    "\n",
    "df_wdb['wind_speed'] = 0 # placeholder value\n",
    "df_wdb['wind_direction'] = -9999 # placeholder value\n",
    "\n",
    "df_wdb['incidence_main'] = -9999 # also missing in sample dataset\n",
    "df_wdb['incidence_second'] = -9999 # also missing in sample dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the timestamp from being an index, to being a regular column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wdb.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform it to the YYYYmmddHHMM format expected by Carnot WDB:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wdb['timestamp'] = df_wdb['timestamp'].dt.strftime(\"%Y%m%d%H%M\").astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wdb['time'] = df_wdb['time'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wdb = df_wdb[['time', 'timestamp', 'zenith', 'azimuth', 'dni', 'dhi', 'OutsideTemp', 'Tsky_rad', 'relative_humidity', 'precipitation', 'cloud_index', 'pressure', 'wind_speed', 'wind_direction', 'aoi', 'incidence_main', 'incidence_second', 'poa_direct', 'poa_diffuse']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Export file to matlab matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "scipy.io.savemat(\"../Data/Exp1_WDB.mat\", {'Exp1_WDB': df_wdb.to_numpy()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['InsideTemp', 'Setpoint', 'OutsideTemp']].plot(figsize = (15, 5))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}