Solar thermal for residential buildings.ipynb

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  {
   "cell_type": "markdown",
   "id": "7e03f110-159b-4288-9c5d-435a4b0b36fa",
   "metadata": {},
   "source": [
    "# Imports and Reading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "11d2a2ca-ac6a-436d-a287-698504553ac6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "pd.set_option('max_columns', 200)\n",
    "\n",
    "import math\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5692ee16-bb9d-428c-a4b7-e86419a78c67",
   "metadata": {},
   "source": [
    "# Raw Solar Data of Cities from PVGIS\n",
    "## Cities include: Brussels, Paris, London, Madrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c5ac4b36-a5c3-4d5c-a548-3a27fc725215",
   "metadata": {},
   "outputs": [],
   "source": [
    "brussels_df = pd.read_csv(r'C:\\Users\\Redd\\Documents\\Python Training\\Portfolio\\2. Solar heating\\Brussels Monthly Data.csv', \n",
    "                delimiter=',',\n",
    "                lineterminator='\\r',\n",
    "                skiprows=(0,1,2,3,4))"
   ]
  },
  {
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   "execution_count": 35,
   "id": "c7fe2dbc-5269-4ed6-9cfb-ee047a3abc85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['\\nyear', 'Unnamed: 1', 'month', 'Unnamed: 3', 'H(h)_m', 'Unnamed: 5',\n",
       "       'H(i_opt)_m', 'Unnamed: 7', 'Hb(n)_m', 'Unnamed: 9', 'Kd',\n",
       "       'Unnamed: 11', 'T2m'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brussels_df.columns"
   ]
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   "cell_type": "code",
   "execution_count": 36,
   "id": "e4addcc7-912d-41b1-b0e8-206cc008b732",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>\\nyear</th>\n",
       "      <th>month</th>\n",
       "      <th>H(h)_m</th>\n",
       "      <th>H(i_opt)_m</th>\n",
       "      <th>Hb(n)_m</th>\n",
       "      <th>Kd</th>\n",
       "      <th>T2m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>\\n2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>21.36</td>\n",
       "      <td>34.69</td>\n",
       "      <td>24.66</td>\n",
       "      <td>0.74</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\\n2018</td>\n",
       "      <td>Feb</td>\n",
       "      <td>59.94</td>\n",
       "      <td>109.66</td>\n",
       "      <td>105.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>\\n2018</td>\n",
       "      <td>Mar</td>\n",
       "      <td>74.49</td>\n",
       "      <td>94.59</td>\n",
       "      <td>65.54</td>\n",
       "      <td>0.60</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>\\n2018</td>\n",
       "      <td>Apr</td>\n",
       "      <td>124.62</td>\n",
       "      <td>141.56</td>\n",
       "      <td>112.51</td>\n",
       "      <td>0.50</td>\n",
       "      <td>12.4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>\\n2018</td>\n",
       "      <td>May</td>\n",
       "      <td>185.03</td>\n",
       "      <td>193.16</td>\n",
       "      <td>177.37</td>\n",
       "      <td>0.39</td>\n",
       "      <td>16.0</td>\n",
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      "text/plain": [
       "   \\nyear month  H(h)_m  H(i_opt)_m  Hb(n)_m    Kd   T2m\n",
       "0  \\n2018   Jan   21.36       34.69    24.66  0.74   5.9\n",
       "1  \\n2018   Feb   59.94      109.66   105.69  0.39   0.5\n",
       "2  \\n2018   Mar   74.49       94.59    65.54  0.60   5.4\n",
       "3  \\n2018   Apr  124.62      141.56   112.51  0.50  12.4\n",
       "4  \\n2018   May  185.03      193.16   177.37  0.39  16.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
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   ],
   "source": [
    "brussels_df = brussels_df[['\\nyear',\n",
    "#                          'Unnamed: 1', \n",
    "                          'month',\n",
    "#                          'Unnamed: 3', \n",
    "                          'H(h)_m',\n",
    "#                          'Unnamed: 5',\n",
    "                          'H(i_opt)_m',\n",
    "#                          'Unnamed: 7', \n",
    "                          'Hb(n)_m',\n",
    "#                          'Unnamed: 9', \n",
    "                          'Kd',\n",
    "#                          'Unnamed: 11',\n",
    "                          'T2m']].copy()\n",
    "brussels_df.head()"
   ]
  },
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   "execution_count": 37,
   "id": "60356ccc-df94-485d-bf3b-571c0f0cd8c3",
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       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>global_horizontal_irradiation</th>\n",
       "      <th>global_irradiation_optimum_angle</th>\n",
       "      <th>direct_normal_irradiation</th>\n",
       "      <th>diffuse_global_ratio</th>\n",
       "      <th>outdoor_temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>\\n2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>21.36</td>\n",
       "      <td>34.69</td>\n",
       "      <td>24.66</td>\n",
       "      <td>0.74</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\\n2018</td>\n",
       "      <td>Feb</td>\n",
       "      <td>59.94</td>\n",
       "      <td>109.66</td>\n",
       "      <td>105.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>\\n2018</td>\n",
       "      <td>Mar</td>\n",
       "      <td>74.49</td>\n",
       "      <td>94.59</td>\n",
       "      <td>65.54</td>\n",
       "      <td>0.60</td>\n",
       "      <td>5.4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>\\n2018</td>\n",
       "      <td>Apr</td>\n",
       "      <td>124.62</td>\n",
       "      <td>141.56</td>\n",
       "      <td>112.51</td>\n",
       "      <td>0.50</td>\n",
       "      <td>12.4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>\\n2018</td>\n",
       "      <td>May</td>\n",
       "      <td>185.03</td>\n",
       "      <td>193.16</td>\n",
       "      <td>177.37</td>\n",
       "      <td>0.39</td>\n",
       "      <td>16.0</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "     year month  global_horizontal_irradiation  \\\n",
       "0  \\n2018   Jan                          21.36   \n",
       "1  \\n2018   Feb                          59.94   \n",
       "2  \\n2018   Mar                          74.49   \n",
       "3  \\n2018   Apr                         124.62   \n",
       "4  \\n2018   May                         185.03   \n",
       "\n",
       "   global_irradiation_optimum_angle  direct_normal_irradiation  \\\n",
       "0                             34.69                      24.66   \n",
       "1                            109.66                     105.69   \n",
       "2                             94.59                      65.54   \n",
       "3                            141.56                     112.51   \n",
       "4                            193.16                     177.37   \n",
       "\n",
       "   diffuse_global_ratio  outdoor_temperature  \n",
       "0                  0.74                  5.9  \n",
       "1                  0.39                  0.5  \n",
       "2                  0.60                  5.4  \n",
       "3                  0.50                 12.4  \n",
       "4                  0.39                 16.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brussels_df = brussels_df.rename(columns = {'\\nyear' : 'year',\n",
    "                                            'month' : 'month',\n",
    "                                            'H(h)_m' : 'global_horizontal_irradiation',\n",
    "                                            'H(i_opt)_m' : 'global_irradiation_optimum_angle',\n",
    "                                            'Hb(n)_m' : 'direct_normal_irradiation',\n",
    "                                            'Kd' : 'diffuse_global_ratio',\n",
    "                                            'T2m' : 'outdoor_temperature'\n",
    "                                           }\n",
    "                                )\n",
    "brussels_df.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 38,
   "id": "15fd5403-92c8-45fb-9180-9d70a9dfda0d",
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   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>global_horizontal_irradiation</th>\n",
       "      <th>global_irradiation_optimum_angle</th>\n",
       "      <th>direct_normal_irradiation</th>\n",
       "      <th>diffuse_global_ratio</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>21.36</td>\n",
       "      <td>34.69</td>\n",
       "      <td>24.66</td>\n",
       "      <td>0.74</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>Feb</td>\n",
       "      <td>59.94</td>\n",
       "      <td>109.66</td>\n",
       "      <td>105.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>Mar</td>\n",
       "      <td>74.49</td>\n",
       "      <td>94.59</td>\n",
       "      <td>65.54</td>\n",
       "      <td>0.60</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>Apr</td>\n",
       "      <td>124.62</td>\n",
       "      <td>141.56</td>\n",
       "      <td>112.51</td>\n",
       "      <td>0.50</td>\n",
       "      <td>12.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>May</td>\n",
       "      <td>185.03</td>\n",
       "      <td>193.16</td>\n",
       "      <td>177.37</td>\n",
       "      <td>0.39</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jun</td>\n",
       "      <td>164.35</td>\n",
       "      <td>158.17</td>\n",
       "      <td>126.23</td>\n",
       "      <td>0.51</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jul</td>\n",
       "      <td>197.61</td>\n",
       "      <td>197.04</td>\n",
       "      <td>182.21</td>\n",
       "      <td>0.41</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018</td>\n",
       "      <td>Aug</td>\n",
       "      <td>145.24</td>\n",
       "      <td>154.85</td>\n",
       "      <td>123.14</td>\n",
       "      <td>0.50</td>\n",
       "      <td>19.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018</td>\n",
       "      <td>Sep</td>\n",
       "      <td>108.98</td>\n",
       "      <td>139.77</td>\n",
       "      <td>113.43</td>\n",
       "      <td>0.50</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "      <td>Oct</td>\n",
       "      <td>74.70</td>\n",
       "      <td>118.61</td>\n",
       "      <td>100.83</td>\n",
       "      <td>0.46</td>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018</td>\n",
       "      <td>Nov</td>\n",
       "      <td>34.23</td>\n",
       "      <td>65.17</td>\n",
       "      <td>55.76</td>\n",
       "      <td>0.55</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018</td>\n",
       "      <td>Dec</td>\n",
       "      <td>20.70</td>\n",
       "      <td>41.79</td>\n",
       "      <td>34.55</td>\n",
       "      <td>0.65</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019</td>\n",
       "      <td>Jan</td>\n",
       "      <td>23.13</td>\n",
       "      <td>40.64</td>\n",
       "      <td>31.89</td>\n",
       "      <td>0.68</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019</td>\n",
       "      <td>Feb</td>\n",
       "      <td>55.56</td>\n",
       "      <td>98.94</td>\n",
       "      <td>93.63</td>\n",
       "      <td>0.41</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019</td>\n",
       "      <td>Mar</td>\n",
       "      <td>76.37</td>\n",
       "      <td>96.48</td>\n",
       "      <td>73.11</td>\n",
       "      <td>0.56</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019</td>\n",
       "      <td>Apr</td>\n",
       "      <td>138.83</td>\n",
       "      <td>161.04</td>\n",
       "      <td>140.54</td>\n",
       "      <td>0.43</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019</td>\n",
       "      <td>May</td>\n",
       "      <td>152.97</td>\n",
       "      <td>153.44</td>\n",
       "      <td>118.39</td>\n",
       "      <td>0.52</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019</td>\n",
       "      <td>Jun</td>\n",
       "      <td>180.53</td>\n",
       "      <td>174.20</td>\n",
       "      <td>159.82</td>\n",
       "      <td>0.44</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019</td>\n",
       "      <td>Jul</td>\n",
       "      <td>172.71</td>\n",
       "      <td>170.06</td>\n",
       "      <td>137.65</td>\n",
       "      <td>0.50</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019</td>\n",
       "      <td>Aug</td>\n",
       "      <td>143.30</td>\n",
       "      <td>156.78</td>\n",
       "      <td>125.03</td>\n",
       "      <td>0.50</td>\n",
       "      <td>19.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2019</td>\n",
       "      <td>Sep</td>\n",
       "      <td>99.03</td>\n",
       "      <td>121.58</td>\n",
       "      <td>92.92</td>\n",
       "      <td>0.54</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>55.73</td>\n",
       "      <td>80.53</td>\n",
       "      <td>55.50</td>\n",
       "      <td>0.64</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019</td>\n",
       "      <td>Nov</td>\n",
       "      <td>32.97</td>\n",
       "      <td>61.67</td>\n",
       "      <td>50.55</td>\n",
       "      <td>0.59</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019</td>\n",
       "      <td>Dec</td>\n",
       "      <td>21.89</td>\n",
       "      <td>44.90</td>\n",
       "      <td>37.36</td>\n",
       "      <td>0.63</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2020</td>\n",
       "      <td>Jan</td>\n",
       "      <td>25.60</td>\n",
       "      <td>49.14</td>\n",
       "      <td>41.78</td>\n",
       "      <td>0.63</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2020</td>\n",
       "      <td>Feb</td>\n",
       "      <td>40.61</td>\n",
       "      <td>61.42</td>\n",
       "      <td>44.96</td>\n",
       "      <td>0.65</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2020</td>\n",
       "      <td>Mar</td>\n",
       "      <td>96.47</td>\n",
       "      <td>131.67</td>\n",
       "      <td>118.93</td>\n",
       "      <td>0.43</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2020</td>\n",
       "      <td>Apr</td>\n",
       "      <td>161.77</td>\n",
       "      <td>195.29</td>\n",
       "      <td>197.17</td>\n",
       "      <td>0.33</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2020</td>\n",
       "      <td>May</td>\n",
       "      <td>198.97</td>\n",
       "      <td>203.76</td>\n",
       "      <td>211.84</td>\n",
       "      <td>0.34</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2020</td>\n",
       "      <td>Jun</td>\n",
       "      <td>168.77</td>\n",
       "      <td>161.40</td>\n",
       "      <td>134.53</td>\n",
       "      <td>0.50</td>\n",
       "      <td>17.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2020</td>\n",
       "      <td>Jul</td>\n",
       "      <td>162.92</td>\n",
       "      <td>159.72</td>\n",
       "      <td>126.29</td>\n",
       "      <td>0.53</td>\n",
       "      <td>17.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2020</td>\n",
       "      <td>Aug</td>\n",
       "      <td>138.90</td>\n",
       "      <td>147.80</td>\n",
       "      <td>108.20</td>\n",
       "      <td>0.53</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2020</td>\n",
       "      <td>Sep</td>\n",
       "      <td>109.29</td>\n",
       "      <td>141.70</td>\n",
       "      <td>121.13</td>\n",
       "      <td>0.44</td>\n",
       "      <td>16.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2020</td>\n",
       "      <td>Oct</td>\n",
       "      <td>49.10</td>\n",
       "      <td>63.66</td>\n",
       "      <td>36.41</td>\n",
       "      <td>0.73</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2020</td>\n",
       "      <td>Nov</td>\n",
       "      <td>33.30</td>\n",
       "      <td>63.14</td>\n",
       "      <td>53.65</td>\n",
       "      <td>0.56</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2020</td>\n",
       "      <td>Dec</td>\n",
       "      <td>19.78</td>\n",
       "      <td>36.50</td>\n",
       "      <td>27.22</td>\n",
       "      <td>0.71</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year month  global_horizontal_irradiation  \\\n",
       "0   2018   Jan                          21.36   \n",
       "1   2018   Feb                          59.94   \n",
       "2   2018   Mar                          74.49   \n",
       "3   2018   Apr                         124.62   \n",
       "4   2018   May                         185.03   \n",
       "5   2018   Jun                         164.35   \n",
       "6   2018   Jul                         197.61   \n",
       "7   2018   Aug                         145.24   \n",
       "8   2018   Sep                         108.98   \n",
       "9   2018   Oct                          74.70   \n",
       "10  2018   Nov                          34.23   \n",
       "11  2018   Dec                          20.70   \n",
       "12  2019   Jan                          23.13   \n",
       "13  2019   Feb                          55.56   \n",
       "14  2019   Mar                          76.37   \n",
       "15  2019   Apr                         138.83   \n",
       "16  2019   May                         152.97   \n",
       "17  2019   Jun                         180.53   \n",
       "18  2019   Jul                         172.71   \n",
       "19  2019   Aug                         143.30   \n",
       "20  2019   Sep                          99.03   \n",
       "21  2019   Oct                          55.73   \n",
       "22  2019   Nov                          32.97   \n",
       "23  2019   Dec                          21.89   \n",
       "24  2020   Jan                          25.60   \n",
       "25  2020   Feb                          40.61   \n",
       "26  2020   Mar                          96.47   \n",
       "27  2020   Apr                         161.77   \n",
       "28  2020   May                         198.97   \n",
       "29  2020   Jun                         168.77   \n",
       "30  2020   Jul                         162.92   \n",
       "31  2020   Aug                         138.90   \n",
       "32  2020   Sep                         109.29   \n",
       "33  2020   Oct                          49.10   \n",
       "34  2020   Nov                          33.30   \n",
       "35  2020   Dec                          19.78   \n",
       "\n",
       "    global_irradiation_optimum_angle  direct_normal_irradiation  \\\n",
       "0                              34.69                      24.66   \n",
       "1                             109.66                     105.69   \n",
       "2                              94.59                      65.54   \n",
       "3                             141.56                     112.51   \n",
       "4                             193.16                     177.37   \n",
       "5                             158.17                     126.23   \n",
       "6                             197.04                     182.21   \n",
       "7                             154.85                     123.14   \n",
       "8                             139.77                     113.43   \n",
       "9                             118.61                     100.83   \n",
       "10                             65.17                      55.76   \n",
       "11                             41.79                      34.55   \n",
       "12                             40.64                      31.89   \n",
       "13                             98.94                      93.63   \n",
       "14                             96.48                      73.11   \n",
       "15                            161.04                     140.54   \n",
       "16                            153.44                     118.39   \n",
       "17                            174.20                     159.82   \n",
       "18                            170.06                     137.65   \n",
       "19                            156.78                     125.03   \n",
       "20                            121.58                      92.92   \n",
       "21                             80.53                      55.50   \n",
       "22                             61.67                      50.55   \n",
       "23                             44.90                      37.36   \n",
       "24                             49.14                      41.78   \n",
       "25                             61.42                      44.96   \n",
       "26                            131.67                     118.93   \n",
       "27                            195.29                     197.17   \n",
       "28                            203.76                     211.84   \n",
       "29                            161.40                     134.53   \n",
       "30                            159.72                     126.29   \n",
       "31                            147.80                     108.20   \n",
       "32                            141.70                     121.13   \n",
       "33                             63.66                      36.41   \n",
       "34                             63.14                      53.65   \n",
       "35                             36.50                      27.22   \n",
       "\n",
       "    diffuse_global_ratio  outdoor_temperature  \n",
       "0                   0.74                  5.9  \n",
       "1                   0.39                  0.5  \n",
       "2                   0.60                  5.4  \n",
       "3                   0.50                 12.4  \n",
       "4                   0.39                 16.0  \n",
       "5                   0.51                 17.9  \n",
       "6                   0.41                 21.8  \n",
       "7                   0.50                 19.3  \n",
       "8                   0.50                 15.2  \n",
       "9                   0.46                 12.7  \n",
       "10                  0.55                  7.4  \n",
       "11                  0.65                  5.9  \n",
       "12                  0.68                  2.7  \n",
       "13                  0.41                  6.2  \n",
       "14                  0.56                  8.2  \n",
       "15                  0.43                 10.3  \n",
       "16                  0.52                 12.0  \n",
       "17                  0.44                 18.5  \n",
       "18                  0.50                 19.5  \n",
       "19                  0.50                 19.1  \n",
       "20                  0.54                 15.5  \n",
       "21                  0.64                 12.3  \n",
       "22                  0.59                  6.3  \n",
       "23                  0.63                  5.8  \n",
       "24                  0.63                  6.0  \n",
       "25                  0.65                  6.9  \n",
       "26                  0.43                  6.7  \n",
       "27                  0.33                 12.2  \n",
       "28                  0.34                 14.2  \n",
       "29                  0.50                 17.3  \n",
       "30                  0.53                 17.8  \n",
       "31                  0.53                 20.8  \n",
       "32                  0.44                 16.6  \n",
       "33                  0.73                 11.7  \n",
       "34                  0.56                  8.9  \n",
       "35                  0.71                  5.4  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brussels_df['year'] = brussels_df['year'].str.replace('\\n', '', regex=False)\n",
    "brussels_df = brussels_df[:36]\n",
    "brussels_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1a0c902f-cb16-482b-be3e-61d95f79fd7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 36 entries, 0 to 35\n",
      "Data columns (total 7 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   year                              36 non-null     object \n",
      " 1   month                             36 non-null     object \n",
      " 2   global_horizontal_irradiation     36 non-null     float64\n",
      " 3   global_irradiation_optimum_angle  36 non-null     float64\n",
      " 4   direct_normal_irradiation         36 non-null     float64\n",
      " 5   diffuse_global_ratio              36 non-null     float64\n",
      " 6   outdoor_temperature               36 non-null     float64\n",
      "dtypes: float64(5), object(2)\n",
      "memory usage: 2.1+ KB\n"
     ]
    }
   ],
   "source": [
    "brussels_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d2dfd8a9-e99f-414b-84c6-d9bddfa3ba59",
   "metadata": {},
   "outputs": [],
   "source": [
    "def month_converter(month):\n",
    "    months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "    return months.index(month) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d6b7a6d3-9af3-4db2-965e-d9dd9b0632c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "brussels_df['month'] = brussels_df['month'].apply(month_converter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0ea8a2ce-5ce5-449f-a6ac-ef3244914f88",
   "metadata": {},
   "outputs": [],
   "source": [
    "brussels_df['year'] = pd.to_numeric(brussels_df['year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a7fd00b9-5080-42d2-aa0c-f5c153ec06e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 36 entries, 0 to 35\n",
      "Data columns (total 7 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   year                              36 non-null     int64  \n",
      " 1   month                             36 non-null     int64  \n",
      " 2   global_horizontal_irradiation     36 non-null     float64\n",
      " 3   global_irradiation_optimum_angle  36 non-null     float64\n",
      " 4   direct_normal_irradiation         36 non-null     float64\n",
      " 5   diffuse_global_ratio              36 non-null     float64\n",
      " 6   outdoor_temperature               36 non-null     float64\n",
      "dtypes: float64(5), int64(2)\n",
      "memory usage: 2.1 KB\n"
     ]
    }
   ],
   "source": [
    "brussels_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "42cd4773-84ba-474b-8997-e32c76526f7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>global_horizontal_irradiation</th>\n",
       "      <th>global_irradiation_optimum_angle</th>\n",
       "      <th>direct_normal_irradiation</th>\n",
       "      <th>diffuse_global_ratio</th>\n",
       "      <th>outdoor_temperature</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>21.36</td>\n",
       "      <td>34.69</td>\n",
       "      <td>24.66</td>\n",
       "      <td>0.74</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2018-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>59.94</td>\n",
       "      <td>109.66</td>\n",
       "      <td>105.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2018-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>74.49</td>\n",
       "      <td>94.59</td>\n",
       "      <td>65.54</td>\n",
       "      <td>0.60</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2018-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>124.62</td>\n",
       "      <td>141.56</td>\n",
       "      <td>112.51</td>\n",
       "      <td>0.50</td>\n",
       "      <td>12.4</td>\n",
       "      <td>2018-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>185.03</td>\n",
       "      <td>193.16</td>\n",
       "      <td>177.37</td>\n",
       "      <td>0.39</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2018-05-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  global_horizontal_irradiation  \\\n",
       "0  2018      1                          21.36   \n",
       "1  2018      2                          59.94   \n",
       "2  2018      3                          74.49   \n",
       "3  2018      4                         124.62   \n",
       "4  2018      5                         185.03   \n",
       "\n",
       "   global_irradiation_optimum_angle  direct_normal_irradiation  \\\n",
       "0                             34.69                      24.66   \n",
       "1                            109.66                     105.69   \n",
       "2                             94.59                      65.54   \n",
       "3                            141.56                     112.51   \n",
       "4                            193.16                     177.37   \n",
       "\n",
       "   diffuse_global_ratio  outdoor_temperature        date  \n",
       "0                  0.74                  5.9  2018-01-01  \n",
       "1                  0.39                  0.5  2018-02-01  \n",
       "2                  0.60                  5.4  2018-03-01  \n",
       "3                  0.50                 12.4  2018-04-01  \n",
       "4                  0.39                 16.0  2018-05-01  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brussels_df['date'] = pd.to_datetime(brussels_df[['year', 'month']].assign(day=1)).dt.date\n",
    "brussels_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "03a2ba61-ab6c-45d2-9cbb-30eff235ca9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>global_horizontal_irradiation</th>\n",
       "      <th>global_irradiation_optimum_angle</th>\n",
       "      <th>direct_normal_irradiation</th>\n",
       "      <th>diffuse_global_ratio</th>\n",
       "      <th>outdoor_temperature</th>\n",
       "      <th>date</th>\n",
       "      <th>calendar_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>138.90</td>\n",
       "      <td>147.80</td>\n",
       "      <td>108.20</td>\n",
       "      <td>0.53</td>\n",
       "      <td>20.8</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>109.29</td>\n",
       "      <td>141.70</td>\n",
       "      <td>121.13</td>\n",
       "      <td>0.44</td>\n",
       "      <td>16.6</td>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>49.10</td>\n",
       "      <td>63.66</td>\n",
       "      <td>36.41</td>\n",
       "      <td>0.73</td>\n",
       "      <td>11.7</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2020</td>\n",
       "      <td>11</td>\n",
       "      <td>33.30</td>\n",
       "      <td>63.14</td>\n",
       "      <td>53.65</td>\n",
       "      <td>0.56</td>\n",
       "      <td>8.9</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>19.78</td>\n",
       "      <td>36.50</td>\n",
       "      <td>27.22</td>\n",
       "      <td>0.71</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  month  global_horizontal_irradiation  \\\n",
       "31  2020      8                         138.90   \n",
       "32  2020      9                         109.29   \n",
       "33  2020     10                          49.10   \n",
       "34  2020     11                          33.30   \n",
       "35  2020     12                          19.78   \n",
       "\n",
       "    global_irradiation_optimum_angle  direct_normal_irradiation  \\\n",
       "31                            147.80                     108.20   \n",
       "32                            141.70                     121.13   \n",
       "33                             63.66                      36.41   \n",
       "34                             63.14                      53.65   \n",
       "35                             36.50                      27.22   \n",
       "\n",
       "    diffuse_global_ratio  outdoor_temperature        date  calendar_days  \n",
       "31                  0.53                 20.8  2020-08-01           31.0  \n",
       "32                  0.44                 16.6  2020-09-01           30.0  \n",
       "33                  0.73                 11.7  2020-10-01           31.0  \n",
       "34                  0.56                  8.9  2020-11-01           30.0  \n",
       "35                  0.71                  5.4  2020-12-01           31.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = brussels_df['date'].diff(periods=-1).dt.days.abs()\n",
    "diff[35] = 31               #initially NaN for the last month value, place 31 for december\n",
    "\n",
    "brussels_df['calendar_days'] = diff\n",
    "brussels_df.tail()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "84bc38e7",
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>global_horizontal_irradiation</th>\n",
       "      <th>global_irradiation_optimum_angle</th>\n",
       "      <th>direct_normal_irradiation</th>\n",
       "      <th>diffuse_global_ratio</th>\n",
       "      <th>outdoor_temperature</th>\n",
       "      <th>date</th>\n",
       "      <th>calendar_days</th>\n",
       "      <th>business_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>138.90</td>\n",
       "      <td>147.80</td>\n",
       "      <td>108.20</td>\n",
       "      <td>0.53</td>\n",
       "      <td>20.8</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>31.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>109.29</td>\n",
       "      <td>141.70</td>\n",
       "      <td>121.13</td>\n",
       "      <td>0.44</td>\n",
       "      <td>16.6</td>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>49.10</td>\n",
       "      <td>63.66</td>\n",
       "      <td>36.41</td>\n",
       "      <td>0.73</td>\n",
       "      <td>11.7</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>31.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2020</td>\n",
       "      <td>11</td>\n",
       "      <td>33.30</td>\n",
       "      <td>63.14</td>\n",
       "      <td>53.65</td>\n",
       "      <td>0.56</td>\n",
       "      <td>8.9</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>30.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>19.78</td>\n",
       "      <td>36.50</td>\n",
       "      <td>27.22</td>\n",
       "      <td>0.71</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>31.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  month  global_horizontal_irradiation  \\\n",
       "31  2020      8                         138.90   \n",
       "32  2020      9                         109.29   \n",
       "33  2020     10                          49.10   \n",
       "34  2020     11                          33.30   \n",
       "35  2020     12                          19.78   \n",
       "\n",
       "    global_irradiation_optimum_angle  direct_normal_irradiation  \\\n",
       "31                            147.80                     108.20   \n",
       "32                            141.70                     121.13   \n",
       "33                             63.66                      36.41   \n",
       "34                             63.14                      53.65   \n",
       "35                             36.50                      27.22   \n",
       "\n",
       "    diffuse_global_ratio  outdoor_temperature        date  calendar_days  \\\n",
       "31                  0.53                 20.8  2020-08-01           31.0   \n",
       "32                  0.44                 16.6  2020-09-01           30.0   \n",
       "33                  0.73                 11.7  2020-10-01           31.0   \n",
       "34                  0.56                  8.9  2020-11-01           30.0   \n",
       "35                  0.71                  5.4  2020-12-01           31.0   \n",
       "\n",
       "    business_days  \n",
       "31             21  \n",
       "32             22  \n",
       "33             22  \n",
       "34             21  \n",
       "35             22  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datelist = [date for date in brussels_df['date']]       #use list to avoid chain indexing\n",
    "bus_days = [np.busday_count(datelist[i],\n",
    "                            datelist[i+1]\n",
    "                            ) for i in range(len(datelist)-1\n",
    "                                            )\n",
    "            ]\n",
    "\n",
    "bus_days.append(bus_days[(35-12)])      #this code is for the last month december 2020 , copying the number of business days of december 2019\n",
    "\n",
    "brussels_df['business_days'] = bus_days\n",
    "brussels_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7fde1c99-0db2-4fd9-944a-83c7cd3e1c66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b2f411e430>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x252 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"darkgrid\")\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = [15, 3.50]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax1 = sns.lineplot(x='date', y='global_horizontal_irradiation', data=brussels_df, label='Global horizontal irradiation')\n",
    "ax2 = sns.lineplot(x='date', y='global_irradiation_optimum_angle', data=brussels_df, label='Global irradiation at Optimum angle')\n",
    "ax3 = sns.lineplot(x='date', y='direct_normal_irradiation', data=brussels_df, label='Direct normal irradiation')\n",
    "ax.set(xlabel='Months', ylabel='Irradiation (kWh/m2)')\n",
    "plt.legend(loc='lower left')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772f249d-38c7-44e9-860d-9c99b066dded",
   "metadata": {},
   "source": [
    "# Generating Water Consumption. Unit: litres of DHWD/day at 60ºC\n",
    "## Reference: Spanish Technical Building Code (CTE)\n",
    "\n",
    "### The litres of DHWD/day at 60ºC in the table have been calculated from Table 1 (average daily unit consumption) of UNE 94002:2005 “Thermal solar systems for domestic hot water production: Calculation method for heat demand.” Equation (3.2) was used for the calculation, with Ti = 12ºC (constant) and T = 45ºC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6ca4e64e-168f-419b-aab4-8f9de893a7cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "water_consumption_per_person = {'single_family_dwellings' : 30,\n",
    "                                'multi_family_dwellings' : 22,\n",
    "                                'hospitals_and_clinics' : 55,\n",
    "                                '4_star_hotel' : 70,\n",
    "                                '3_star_hotel' : 55,\n",
    "                                '2_star_hotel' : 40,\n",
    "                                'hostel' : 40,\n",
    "                                'camping' : 40,\n",
    "                                'boarding_house' : 35,\n",
    "                                'homes_for_the_elderly' : 55,\n",
    "                                'student_dormitories' : 55,\n",
    "                                'dressing_rooms' : 15,\n",
    "                                'schools' : 3,\n",
    "                                'barracks' : 20,\n",
    "                                'factories_and_shops' : 15,\n",
    "                                'administrative_premises' : 3,\n",
    "                                'gyms' : 22.5,\n",
    "                                'restaurants' : 7.5,\n",
    "                                'cafeterias' : 1\n",
    "                               }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9f3dff0e-427b-4d31-a113-f794ebe85285",
   "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>room_name</th>\n",
       "      <th>room_type</th>\n",
       "      <th>room_count</th>\n",
       "      <th>people_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>library</td>\n",
       "      <td>schools</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>office</td>\n",
       "      <td>administrative_premises</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  room_name                room_type  room_count  people_count\n",
       "0   library                  schools           1            30\n",
       "1    office  administrative_premises           4             4"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [('library', 'schools', 1, 30),\n",
    "        ('office', 'administrative_premises', 4, 4)]\n",
    "b1_df = pd.DataFrame(data,\n",
    "                     columns=['room_name', 'room_type', 'room_count', 'people_count'],\n",
    "                    )\n",
    "# b1_df.set_index('room_name',\n",
    "#                 inplace = True\n",
    "#                )\n",
    "b1_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "afea5d5b-3712-4f9f-b55f-71f8fb3c0410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'room_name': 'library',\n",
       "  'room_type': 'schools',\n",
       "  'room_count': 1,\n",
       "  'people_count': 30},\n",
       " {'room_name': 'office',\n",
       "  'room_type': 'administrative_premises',\n",
       "  'room_count': 4,\n",
       "  'people_count': 4}]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b1_dict = b1_df.to_dict(orient='records')\n",
    "b1_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "21d6c978-9763-49fa-a4a6-d1bdc3c26385",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Room:\n",
    "    def __init__(self, room_name, room_type, room_count, people_count):\n",
    "        self.room_name = room_name\n",
    "        self.room_type = room_type\n",
    "        self.room_count = room_count\n",
    "        self.people_count = people_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ffe61961-e013-47be-ac74-773cc142db2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getWater(room_type, room_count, people_count):\n",
    "    if room_type in water_consumption_per_person.keys():\n",
    "        water_consumption = water_consumption_per_person[room_type] * room_count * people_count            \n",
    "    else:\n",
    "        print('Please choose from building types from CTE')\n",
    "        water_consumption = 0\n",
    "    return water_consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "13adbcbe-5058-41ab-8dab-aca769c81e1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "138"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "room_list = [Room(**x) for x in b1_dict]\n",
    "b1_water = 0\n",
    "for room in room_list:\n",
    "    b1_water += getWater(room.room_type, room.room_count, room.people_count)\n",
    "b1_water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a723d6aa-e3db-4aba-a299-490de17af53c",
   "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>room_name</th>\n",
       "      <th>room_type</th>\n",
       "      <th>room_count</th>\n",
       "      <th>people_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>library</td>\n",
       "      <td>schools</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dining_room</td>\n",
       "      <td>restaurants</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     room_name    room_type  room_count  people_count\n",
       "0      library      schools           1            38\n",
       "1  dining_room  restaurants           1            38"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [('library', 'schools', 1, 38),\n",
    "        ('dining_room', 'restaurants', 1, 38)]\n",
    "b2_df = pd.DataFrame(data,\n",
    "                     columns=['room_name', 'room_type', 'room_count', 'people_count'],\n",
    "                    )\n",
    "b2_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f3440b07-ce60-4440-8b27-62263f2b746f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'room_name': 'library',\n",
       "  'room_type': 'schools',\n",
       "  'room_count': 1,\n",
       "  'people_count': 38},\n",
       " {'room_name': 'dining_room',\n",
       "  'room_type': 'restaurants',\n",
       "  'room_count': 1,\n",
       "  'people_count': 38}]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b2_dict = b2_df.to_dict(orient='records')\n",
    "b2_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "50779d20-00dd-4366-8294-2fd2640bebc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "399.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "room_list = [Room(**x) for x in b2_dict]\n",
    "b2_water = 0\n",
    "for room in room_list:\n",
    "    b2_water += getWater(room.room_type, room.room_count, room.people_count)\n",
    "b2_water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8b7a1373-baff-4ada-8ddb-08f558e666fd",
   "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>room_name</th>\n",
       "      <th>room_type</th>\n",
       "      <th>room_count</th>\n",
       "      <th>people_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gym</td>\n",
       "      <td>gyms</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fronton</td>\n",
       "      <td>gyms</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  room_name room_type  room_count  people_count\n",
       "0       gym      gyms           1            10\n",
       "1   fronton      gyms           1            25"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [('gym', 'gyms', 1, 10),\n",
    "        ('fronton', 'gyms', 1, 25)]\n",
    "b3_df = pd.DataFrame(data,\n",
    "                     columns=['room_name', 'room_type', 'room_count', 'people_count'],\n",
    "                    )\n",
    "b3_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3de59435-7f5c-4c45-a18a-9d18f024d8b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'room_name': 'gym',\n",
       "  'room_type': 'gyms',\n",
       "  'room_count': 1,\n",
       "  'people_count': 10},\n",
       " {'room_name': 'fronton',\n",
       "  'room_type': 'gyms',\n",
       "  'room_count': 1,\n",
       "  'people_count': 25}]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b3_dict = b3_df.to_dict(orient='records')\n",
    "b3_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3c0dee09-827c-4192-8584-3123ae29d767",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "787.5"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "room_list = [Room(**x) for x in b3_dict]\n",
    "b3_water = 0\n",
    "for room in room_list:\n",
    "    b3_water += getWater(room.room_type, room.room_count, room.people_count)\n",
    "b3_water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1677227d-9236-491a-a924-ad45452250f8",
   "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>room_name</th>\n",
       "      <th>room_type</th>\n",
       "      <th>room_count</th>\n",
       "      <th>people_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dwellings</td>\n",
       "      <td>single_family_dwellings</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>restaurant</td>\n",
       "      <td>restaurants</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hostel</td>\n",
       "      <td>hostel</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    room_name                room_type  room_count  people_count\n",
       "0   dwellings  single_family_dwellings           4             4\n",
       "1  restaurant              restaurants           1            20\n",
       "2      hostel                   hostel           1            15"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [('dwellings', 'single_family_dwellings', 4, 4),\n",
    "        ('restaurant', 'restaurants', 1, 20), \n",
    "        ('hostel', 'hostel', 1, 15)]\n",
    "b4_df = pd.DataFrame(data,\n",
    "                     columns=['room_name', 'room_type', 'room_count', 'people_count'],\n",
    "                    )\n",
    "b4_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "98509a5d-f7d4-421f-874f-18c644382a1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'room_name': 'dwellings',\n",
       "  'room_type': 'single_family_dwellings',\n",
       "  'room_count': 4,\n",
       "  'people_count': 4},\n",
       " {'room_name': 'restaurant',\n",
       "  'room_type': 'restaurants',\n",
       "  'room_count': 1,\n",
       "  'people_count': 20},\n",
       " {'room_name': 'hostel',\n",
       "  'room_type': 'hostel',\n",
       "  'room_count': 1,\n",
       "  'people_count': 15}]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b4_dict = b4_df.to_dict(orient='records')\n",
    "b4_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "81ed10d8-4e1e-4a34-9e5c-120b45699f31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1230.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "room_list = [Room(**x) for x in b4_dict]\n",
    "b4_water = 0\n",
    "for room in room_list:\n",
    "    b4_water += getWater(room.room_type, room.room_count, room.people_count)\n",
    "b4_water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "070769ba-1045-420a-82fc-cb9f4f1bc9d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2554.5"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total water consumption for four (4) buildings in the disctrict\n",
    "total_water = b1_water + b2_water + b3_water + b4_water\n",
    "total_water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "01c423b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>calendar_days</th>\n",
       "      <th>mwt</th>\n",
       "      <th>b1</th>\n",
       "      <th>b2</th>\n",
       "      <th>b3</th>\n",
       "      <th>b4</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31.0</td>\n",
       "      <td>9</td>\n",
       "      <td>913293108.0</td>\n",
       "      <td>2.640608e+09</td>\n",
       "      <td>5.211727e+09</td>\n",
       "      <td>8.140221e+09</td>\n",
       "      <td>1.690585e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.0</td>\n",
       "      <td>10</td>\n",
       "      <td>808735200.0</td>\n",
       "      <td>2.338300e+09</td>\n",
       "      <td>4.615065e+09</td>\n",
       "      <td>7.208292e+09</td>\n",
       "      <td>1.497039e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.0</td>\n",
       "      <td>10</td>\n",
       "      <td>895385400.0</td>\n",
       "      <td>2.588832e+09</td>\n",
       "      <td>5.109536e+09</td>\n",
       "      <td>7.980609e+09</td>\n",
       "      <td>1.657436e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.0</td>\n",
       "      <td>11</td>\n",
       "      <td>849171960.0</td>\n",
       "      <td>2.455215e+09</td>\n",
       "      <td>4.845818e+09</td>\n",
       "      <td>7.568707e+09</td>\n",
       "      <td>1.571891e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31.0</td>\n",
       "      <td>13</td>\n",
       "      <td>841662276.0</td>\n",
       "      <td>2.433502e+09</td>\n",
       "      <td>4.802964e+09</td>\n",
       "      <td>7.501772e+09</td>\n",
       "      <td>1.557990e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30.0</td>\n",
       "      <td>15</td>\n",
       "      <td>779851800.0</td>\n",
       "      <td>2.254789e+09</td>\n",
       "      <td>4.450241e+09</td>\n",
       "      <td>6.950853e+09</td>\n",
       "      <td>1.443573e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31.0</td>\n",
       "      <td>17</td>\n",
       "      <td>770031444.0</td>\n",
       "      <td>2.226395e+09</td>\n",
       "      <td>4.394201e+09</td>\n",
       "      <td>6.863324e+09</td>\n",
       "      <td>1.425395e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>31.0</td>\n",
       "      <td>17</td>\n",
       "      <td>770031444.0</td>\n",
       "      <td>2.226395e+09</td>\n",
       "      <td>4.394201e+09</td>\n",
       "      <td>6.863324e+09</td>\n",
       "      <td>1.425395e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>30.0</td>\n",
       "      <td>16</td>\n",
       "      <td>762521760.0</td>\n",
       "      <td>2.204682e+09</td>\n",
       "      <td>4.351347e+09</td>\n",
       "      <td>6.796390e+09</td>\n",
       "      <td>1.411494e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>31.0</td>\n",
       "      <td>14</td>\n",
       "      <td>823754568.0</td>\n",
       "      <td>2.381725e+09</td>\n",
       "      <td>4.700773e+09</td>\n",
       "      <td>7.342160e+09</td>\n",
       "      <td>1.524841e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>30.0</td>\n",
       "      <td>11</td>\n",
       "      <td>849171960.0</td>\n",
       "      <td>2.455215e+09</td>\n",
       "      <td>4.845818e+09</td>\n",
       "      <td>7.568707e+09</td>\n",
       "      <td>1.571891e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>31.0</td>\n",
       "      <td>10</td>\n",
       "      <td>895385400.0</td>\n",
       "      <td>2.588832e+09</td>\n",
       "      <td>5.109536e+09</td>\n",
       "      <td>7.980609e+09</td>\n",
       "      <td>1.657436e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    calendar_days  mwt           b1            b2            b3            b4  \\\n",
       "0            31.0    9  913293108.0  2.640608e+09  5.211727e+09  8.140221e+09   \n",
       "1            28.0   10  808735200.0  2.338300e+09  4.615065e+09  7.208292e+09   \n",
       "2            31.0   10  895385400.0  2.588832e+09  5.109536e+09  7.980609e+09   \n",
       "3            30.0   11  849171960.0  2.455215e+09  4.845818e+09  7.568707e+09   \n",
       "4            31.0   13  841662276.0  2.433502e+09  4.802964e+09  7.501772e+09   \n",
       "5            30.0   15  779851800.0  2.254789e+09  4.450241e+09  6.950853e+09   \n",
       "6            31.0   17  770031444.0  2.226395e+09  4.394201e+09  6.863324e+09   \n",
       "7            31.0   17  770031444.0  2.226395e+09  4.394201e+09  6.863324e+09   \n",
       "8            30.0   16  762521760.0  2.204682e+09  4.351347e+09  6.796390e+09   \n",
       "9            31.0   14  823754568.0  2.381725e+09  4.700773e+09  7.342160e+09   \n",
       "10           30.0   11  849171960.0  2.455215e+09  4.845818e+09  7.568707e+09   \n",
       "11           31.0   10  895385400.0  2.588832e+09  5.109536e+09  7.980609e+09   \n",
       "\n",
       "           total  \n",
       "0   1.690585e+10  \n",
       "1   1.497039e+10  \n",
       "2   1.657436e+10  \n",
       "3   1.571891e+10  \n",
       "4   1.557990e+10  \n",
       "5   1.443573e+10  \n",
       "6   1.425395e+10  \n",
       "7   1.425395e+10  \n",
       "8   1.411494e+10  \n",
       "9   1.524841e+10  \n",
       "10  1.571891e+10  \n",
       "11  1.657436e+10  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = 4186       #specific heat of water at J/kg-K\n",
    "hwt = 60        #hot water temperature requirement at degC\n",
    "\n",
    "#calculating demand in Joules, Q = m*c*deltaT*no.days\n",
    "\n",
    "demand = pd.DataFrame()\n",
    "demand['calendar_days'] = brussels_df['calendar_days'][0:12]\n",
    "demand['mwt'] = [9,10,10,11,13,15,17,17,16,14,11,10]\n",
    "demand['b1'] = b1_water*c*(hwt - demand['mwt'])*demand['calendar_days']\n",
    "demand['b2'] = b2_water*c*(hwt - demand['mwt'])*demand['calendar_days']\n",
    "demand['b3'] = b3_water*c*(hwt - demand['mwt'])*demand['calendar_days']\n",
    "demand['b4'] = b4_water*c*(hwt - demand['mwt'])*demand['calendar_days']\n",
    "demand['total'] = demand['b1'] + demand['b2'] + demand['b3'] + demand['b4']\n",
    "demand"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d31adf86-6951-4972-8629-c4a350cc1f33",
   "metadata": {},
   "source": [
    "# Calculation of Solar Thermal Area based on available solar thermal brands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "0e47d22f-9538-4e12-a24b-247407b6c4e2",
   "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>model</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>aperture_area</th>\n",
       "      <th>tau_alpha</th>\n",
       "      <th>k1</th>\n",
       "      <th>k2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AP-30</td>\n",
       "      <td>2.005</td>\n",
       "      <td>2.195</td>\n",
       "      <td>2.83</td>\n",
       "      <td>0.687</td>\n",
       "      <td>1.505</td>\n",
       "      <td>0.0111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Promatop 1.5</td>\n",
       "      <td>1.510</td>\n",
       "      <td>1.010</td>\n",
       "      <td>1.34</td>\n",
       "      <td>0.760</td>\n",
       "      <td>4.540</td>\n",
       "      <td>0.0120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Promatop 2.6</td>\n",
       "      <td>2.005</td>\n",
       "      <td>2.196</td>\n",
       "      <td>2.83</td>\n",
       "      <td>0.687</td>\n",
       "      <td>1.505</td>\n",
       "      <td>0.0111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          model  length  width  aperture_area  tau_alpha     k1      k2\n",
       "0         AP-30   2.005  2.195           2.83      0.687  1.505  0.0111\n",
       "1  Promatop 1.5   1.510  1.010           1.34      0.760  4.540  0.0120\n",
       "2  Promatop 2.6   2.005  2.196           2.83      0.687  1.505  0.0111"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [('AP-30', 2.005, 2.195, 2.83, 0.687, 1.505, 0.0111),\n",
    "        ('Promatop 1.5', 1.51, 1.01, 1.34, 0.76, 4.54, 0.012), \n",
    "        ('Promatop 2.6', 2.005, 2.196, 2.83, 0.687, 1.505, 0.0111)\n",
    "#additional brands can be added here   \n",
    "       ]\n",
    "\n",
    "solar_df = pd.DataFrame(data,\n",
    "                     columns=['model', 'length', 'width', 'aperture_area', 'tau_alpha', 'k1', 'k2',],\n",
    "                    )\n",
    "solar_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a36d8b90-a04b-4e88-96bb-05ce058c3d2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'model': 'AP-30',\n",
       "  'length': 2.005,\n",
       "  'width': 2.195,\n",
       "  'aperture_area': 2.83,\n",
       "  'tau_alpha': 0.687,\n",
       "  'k1': 1.505,\n",
       "  'k2': 0.0111},\n",
       " {'model': 'Promatop 1.5',\n",
       "  'length': 1.51,\n",
       "  'width': 1.01,\n",
       "  'aperture_area': 1.34,\n",
       "  'tau_alpha': 0.76,\n",
       "  'k1': 4.54,\n",
       "  'k2': 0.012},\n",
       " {'model': 'Promatop 2.6',\n",
       "  'length': 2.005,\n",
       "  'width': 2.196,\n",
       "  'aperture_area': 2.83,\n",
       "  'tau_alpha': 0.687,\n",
       "  'k1': 1.505,\n",
       "  'k2': 0.0111}]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar_dict = solar_df.to_dict(orient='records')\n",
    "solar_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "81f41286-a704-40e5-a1eb-05693116e207",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AP-30'"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar_dict[0]['model']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "978147e0-adc5-4022-bb56-6c148ba7fa10",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "#latitude - latitude of the palce\n",
    "#declination at worst day = -23.45 degrees\n",
    "#solar altitude at noon = 23.284 degrees\n",
    "\n",
    "#col_hor = horizontal dimension (size) of the collector\n",
    "\n",
    "def distancing(slope_collector, slope_roof, col_hor):\n",
    "    #conversion to radians\n",
    "#    declination = -23.45 * math.pi / 180\n",
    "    altitude = 23.284 * math.pi / 180\n",
    "#    if -90 <= lat <= 90: \n",
    "#        lat = lat * math.pi / 180\n",
    "    if 0 <= slope_collector <= 90: \n",
    "        slope_collector = slope_collector * math.pi / 180\n",
    "    if 0 <= slope_roof <= 90:\n",
    "        slope_roof = slope_roof * math.pi / 180\n",
    "\n",
    "    #security factor, s \n",
    "    if slope_collector == slope_roof:\n",
    "        s = 1\n",
    "    else:\n",
    "        s = 1.25\n",
    "        \n",
    "    d1 = col_hor * math.sin(slope_collector - slope_roof) / math.tan(altitude + slope_roof)\n",
    "    d2 = col_hor * math.cos(slope_collector - slope_roof)\n",
    "    d = s * (d1 + d2)\n",
    "\n",
    "    return d\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d19c92ff-33f4-4d00-859d-1bdc0298384b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.9978750371689995"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distancing(45, 35, 2.196)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "84a2438f-457f-48d4-9ae4-b3fbea7bf163",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#roof_hor = horizontal dimension (size) of the roof\n",
    "#roof_ver = vertical dimension (size) of the roof\n",
    "#col_hor = horizontal dimension (size) of the collector\n",
    "#col_hor = vertical dimension (size) of the collector\n",
    "irradiance_area = 0\n",
    "\n",
    "def no_collector(roof_hor, roof_ver, col_hor, col_ver):\n",
    "    roof_area = roof_hor * roof_ver\n",
    "    col_area = col_hor * col_ver\n",
    "    \n",
    "    columns = roof_hor // col_ver\n",
    "    rows = roof_ver // col_hor\n",
    "    total_no = columns * rows\n",
    "    global irradiance_area\n",
    "    irradiance_area = total_no * col_area\n",
    "    \n",
    "    return print(f'The total number of solar panels is {total_no}.'), print(f'The total solar panel area is {irradiance_area} m2.')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b85a5e15-b33b-4af2-9c67-0f2f4fb46899",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of solar panels is 16.0.\n",
      "The total solar panel area is 70.44768 m2.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_collector(17, 6, 2.196, 2.005)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "328baadc",
   "metadata": {},
   "source": [
    "# Start Calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "5a712d99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>index</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>global_horizontal_irradiation</th>\n",
       "      <th>global_irradiation_optimum_angle</th>\n",
       "      <th>direct_normal_irradiation</th>\n",
       "      <th>diffuse_global_ratio</th>\n",
       "      <th>outdoor_temperature</th>\n",
       "      <th>date</th>\n",
       "      <th>calendar_days</th>\n",
       "      <th>business_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>23.13</td>\n",
       "      <td>40.64</td>\n",
       "      <td>31.89</td>\n",
       "      <td>0.68</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>31.0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>55.56</td>\n",
       "      <td>98.94</td>\n",
       "      <td>93.63</td>\n",
       "      <td>0.41</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>28.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>76.37</td>\n",
       "      <td>96.48</td>\n",
       "      <td>73.11</td>\n",
       "      <td>0.56</td>\n",
       "      <td>8.2</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>31.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>138.83</td>\n",
       "      <td>161.04</td>\n",
       "      <td>140.54</td>\n",
       "      <td>0.43</td>\n",
       "      <td>10.3</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>152.97</td>\n",
       "      <td>153.44</td>\n",
       "      <td>118.39</td>\n",
       "      <td>0.52</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>31.0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>180.53</td>\n",
       "      <td>174.20</td>\n",
       "      <td>159.82</td>\n",
       "      <td>0.44</td>\n",
       "      <td>18.5</td>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>172.71</td>\n",
       "      <td>170.06</td>\n",
       "      <td>137.65</td>\n",
       "      <td>0.50</td>\n",
       "      <td>19.5</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>31.0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>143.30</td>\n",
       "      <td>156.78</td>\n",
       "      <td>125.03</td>\n",
       "      <td>0.50</td>\n",
       "      <td>19.1</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>31.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>99.03</td>\n",
       "      <td>121.58</td>\n",
       "      <td>92.92</td>\n",
       "      <td>0.54</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>30.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>55.73</td>\n",
       "      <td>80.53</td>\n",
       "      <td>55.50</td>\n",
       "      <td>0.64</td>\n",
       "      <td>12.3</td>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>31.0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>22</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>32.97</td>\n",
       "      <td>61.67</td>\n",
       "      <td>50.55</td>\n",
       "      <td>0.59</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>30.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>23</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>21.89</td>\n",
       "      <td>44.90</td>\n",
       "      <td>37.36</td>\n",
       "      <td>0.63</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>31.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  year  month  global_horizontal_irradiation  \\\n",
       "0      12  2019      1                          23.13   \n",
       "1      13  2019      2                          55.56   \n",
       "2      14  2019      3                          76.37   \n",
       "3      15  2019      4                         138.83   \n",
       "4      16  2019      5                         152.97   \n",
       "5      17  2019      6                         180.53   \n",
       "6      18  2019      7                         172.71   \n",
       "7      19  2019      8                         143.30   \n",
       "8      20  2019      9                          99.03   \n",
       "9      21  2019     10                          55.73   \n",
       "10     22  2019     11                          32.97   \n",
       "11     23  2019     12                          21.89   \n",
       "\n",
       "    global_irradiation_optimum_angle  direct_normal_irradiation  \\\n",
       "0                              40.64                      31.89   \n",
       "1                              98.94                      93.63   \n",
       "2                              96.48                      73.11   \n",
       "3                             161.04                     140.54   \n",
       "4                             153.44                     118.39   \n",
       "5                             174.20                     159.82   \n",
       "6                             170.06                     137.65   \n",
       "7                             156.78                     125.03   \n",
       "8                             121.58                      92.92   \n",
       "9                              80.53                      55.50   \n",
       "10                             61.67                      50.55   \n",
       "11                             44.90                      37.36   \n",
       "\n",
       "    diffuse_global_ratio  outdoor_temperature        date  calendar_days  \\\n",
       "0                   0.68                  2.7  2019-01-01           31.0   \n",
       "1                   0.41                  6.2  2019-02-01           28.0   \n",
       "2                   0.56                  8.2  2019-03-01           31.0   \n",
       "3                   0.43                 10.3  2019-04-01           30.0   \n",
       "4                   0.52                 12.0  2019-05-01           31.0   \n",
       "5                   0.44                 18.5  2019-06-01           30.0   \n",
       "6                   0.50                 19.5  2019-07-01           31.0   \n",
       "7                   0.50                 19.1  2019-08-01           31.0   \n",
       "8                   0.54                 15.5  2019-09-01           30.0   \n",
       "9                   0.64                 12.3  2019-10-01           31.0   \n",
       "10                  0.59                  6.3  2019-11-01           30.0   \n",
       "11                  0.63                  5.8  2019-12-01           31.0   \n",
       "\n",
       "    business_days  \n",
       "0              23  \n",
       "1              20  \n",
       "2              21  \n",
       "3              22  \n",
       "4              23  \n",
       "5              20  \n",
       "6              23  \n",
       "7              22  \n",
       "8              21  \n",
       "9              23  \n",
       "10             21  \n",
       "11             22  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose the the year for consideration\n",
    "year = 2019\n",
    "calcdf = brussels_df[brussels_df['year'] == year]\n",
    "calcdf.reset_index(inplace=True)\n",
    "calcdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "229bc701",
   "metadata": {},
   "outputs": [],
   "source": [
    "calcdf = calcdf[['year', 'month', 'global_horizontal_irradiation',\n",
    "#       'global_irradiation_optimum_angle', 'direct_normal_irradiation',\n",
    "#       'diffuse_global_ratio', \n",
    "                'outdoor_temperature', 'date', 'calendar_days',\n",
    "                'business_days'\n",
    "               ]].copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "92fe950e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>year</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
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       "      <td>23.13</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
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       "      <td>10.3</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22</td>\n",
       "      <td>1.665960e+07</td>\n",
       "      <td>1.571891e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>152.97</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2019-05-01</td>\n",
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       "      <td>23</td>\n",
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       "      <td>1.557990e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>180.53</td>\n",
       "      <td>18.5</td>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20</td>\n",
       "      <td>2.166360e+07</td>\n",
       "      <td>1.443573e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>172.71</td>\n",
       "      <td>19.5</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>31.0</td>\n",
       "      <td>23</td>\n",
       "      <td>2.005665e+07</td>\n",
       "      <td>1.425395e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>143.30</td>\n",
       "      <td>19.1</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>31.0</td>\n",
       "      <td>22</td>\n",
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       "      <td>1.425395e+10</td>\n",
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       "      <th>8</th>\n",
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       "      <td>21</td>\n",
       "      <td>1.188360e+07</td>\n",
       "      <td>1.411494e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>55.73</td>\n",
       "      <td>12.3</td>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>31.0</td>\n",
       "      <td>23</td>\n",
       "      <td>6.471871e+06</td>\n",
       "      <td>1.524841e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>32.97</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2019-11-01</td>\n",
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      ],
      "text/plain": [
       "    year  month  global_horizontal_irradiation  outdoor_temperature  \\\n",
       "0   2019      1                          23.13                  2.7   \n",
       "1   2019      2                          55.56                  6.2   \n",
       "2   2019      3                          76.37                  8.2   \n",
       "3   2019      4                         138.83                 10.3   \n",
       "4   2019      5                         152.97                 12.0   \n",
       "5   2019      6                         180.53                 18.5   \n",
       "6   2019      7                         172.71                 19.5   \n",
       "7   2019      8                         143.30                 19.1   \n",
       "8   2019      9                          99.03                 15.5   \n",
       "9   2019     10                          55.73                 12.3   \n",
       "10  2019     11                          32.97                  6.3   \n",
       "11  2019     12                          21.89                  5.8   \n",
       "\n",
       "          date  calendar_days  business_days             H             L  \n",
       "0   2019-01-01           31.0             23  2.686065e+06  1.690585e+10  \n",
       "1   2019-02-01           28.0             20  7.143429e+06  1.497039e+10  \n",
       "2   2019-03-01           31.0             21  8.868774e+06  1.657436e+10  \n",
       "3   2019-04-01           30.0             22  1.665960e+07  1.571891e+10  \n",
       "4   2019-05-01           31.0             23  1.776426e+07  1.557990e+10  \n",
       "5   2019-06-01           30.0             20  2.166360e+07  1.443573e+10  \n",
       "6   2019-07-01           31.0             23  2.005665e+07  1.425395e+10  \n",
       "7   2019-08-01           31.0             22  1.664129e+07  1.425395e+10  \n",
       "8   2019-09-01           30.0             21  1.188360e+07  1.411494e+10  \n",
       "9   2019-10-01           31.0             23  6.471871e+06  1.524841e+10  \n",
       "10  2019-11-01           30.0             21  3.956400e+06  1.571891e+10  \n",
       "11  2019-12-01           31.0             22  2.542065e+06  1.657436e+10  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#conversion from kWh/m2/mo to J/m2/day : 1kWh(1000Wh/1kWh)(3600s/1h) = 3600000 J\n",
    "\n",
    "calcdf['H'] = (calcdf['global_horizontal_irradiation']/calcdf['calendar_days'])*3600000     #units in J/m2/day\n",
    "calcdf['L'] = demand['total']               #units in J\n",
    "calcdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "922f86f7",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <td>2019</td>\n",
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       "      <td>1.557990e+10</td>\n",
       "      <td>1.507733</td>\n",
       "      <td>13</td>\n",
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       "      <td>1.794545</td>\n",
       "      <td>0.997135</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>180.53</td>\n",
       "      <td>18.5</td>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20</td>\n",
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       "      <td>1.458427</td>\n",
       "      <td>15</td>\n",
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       "      <td>1.742597</td>\n",
       "      <td>1.154387</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
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       "      <td>1.924774</td>\n",
       "      <td>1.123546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>143.30</td>\n",
       "      <td>19.1</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>31.0</td>\n",
       "      <td>22</td>\n",
       "      <td>1.664129e+07</td>\n",
       "      <td>1.425395e+10</td>\n",
       "      <td>1.515025</td>\n",
       "      <td>17</td>\n",
       "      <td>1.646511</td>\n",
       "      <td>1.942153</td>\n",
       "      <td>1.003291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>99.03</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>30.0</td>\n",
       "      <td>21</td>\n",
       "      <td>1.188360e+07</td>\n",
       "      <td>1.411494e+10</td>\n",
       "      <td>1.546478</td>\n",
       "      <td>16</td>\n",
       "      <td>1.149057</td>\n",
       "      <td>1.980224</td>\n",
       "      <td>0.766367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>55.73</td>\n",
       "      <td>12.3</td>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>31.0</td>\n",
       "      <td>23</td>\n",
       "      <td>6.471871e+06</td>\n",
       "      <td>1.524841e+10</td>\n",
       "      <td>1.535258</td>\n",
       "      <td>14</td>\n",
       "      <td>0.598574</td>\n",
       "      <td>1.888945</td>\n",
       "      <td>0.413381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>32.97</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>30.0</td>\n",
       "      <td>21</td>\n",
       "      <td>3.956400e+06</td>\n",
       "      <td>1.571891e+10</td>\n",
       "      <td>1.539867</td>\n",
       "      <td>11</td>\n",
       "      <td>0.343519</td>\n",
       "      <td>1.811751</td>\n",
       "      <td>0.210938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>21.89</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>31.0</td>\n",
       "      <td>22</td>\n",
       "      <td>2.542065e+06</td>\n",
       "      <td>1.657436e+10</td>\n",
       "      <td>1.517122</td>\n",
       "      <td>10</td>\n",
       "      <td>0.216303</td>\n",
       "      <td>1.732032</td>\n",
       "      <td>0.101866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  month  global_horizontal_irradiation  outdoor_temperature  \\\n",
       "0   2019      1                          23.13                  2.7   \n",
       "1   2019      2                          55.56                  6.2   \n",
       "2   2019      3                          76.37                  8.2   \n",
       "3   2019      4                         138.83                 10.3   \n",
       "4   2019      5                         152.97                 12.0   \n",
       "5   2019      6                         180.53                 18.5   \n",
       "6   2019      7                         172.71                 19.5   \n",
       "7   2019      8                         143.30                 19.1   \n",
       "8   2019      9                          99.03                 15.5   \n",
       "9   2019     10                          55.73                 12.3   \n",
       "10  2019     11                          32.97                  6.3   \n",
       "11  2019     12                          21.89                  5.8   \n",
       "\n",
       "          date  calendar_days  business_days             H             L  \\\n",
       "0   2019-01-01           31.0             23  2.686065e+06  1.690585e+10   \n",
       "1   2019-02-01           28.0             20  7.143429e+06  1.497039e+10   \n",
       "2   2019-03-01           31.0             21  8.868774e+06  1.657436e+10   \n",
       "3   2019-04-01           30.0             22  1.665960e+07  1.571891e+10   \n",
       "4   2019-05-01           31.0             23  1.776426e+07  1.557990e+10   \n",
       "5   2019-06-01           30.0             20  2.166360e+07  1.443573e+10   \n",
       "6   2019-07-01           31.0             23  2.005665e+07  1.425395e+10   \n",
       "7   2019-08-01           31.0             22  1.664129e+07  1.425395e+10   \n",
       "8   2019-09-01           30.0             21  1.188360e+07  1.411494e+10   \n",
       "9   2019-10-01           31.0             23  6.471871e+06  1.524841e+10   \n",
       "10  2019-11-01           30.0             21  3.956400e+06  1.571891e+10   \n",
       "11  2019-12-01           31.0             22  2.542065e+06  1.657436e+10   \n",
       "\n",
       "           X  mwt         Y        X'         f  \n",
       "0   1.536322    9  0.224074  1.750681  0.107870  \n",
       "1   1.510680   10  0.607831  1.717086  0.431249  \n",
       "2   1.478469   10  0.754640  1.642357  0.542444  \n",
       "3   1.474131   11  1.446487  1.659244  0.936022  \n",
       "4   1.507733   13  1.608035  1.794545  0.997135  \n",
       "5   1.458427   15  2.048162  1.742597  1.154387  \n",
       "6   1.507534   17  1.984431  1.924774  1.123546  \n",
       "7   1.515025   17  1.646511  1.942153  1.003291  \n",
       "8   1.546478   16  1.149057  1.980224  0.766367  \n",
       "9   1.535258   14  0.598574  1.888945  0.413381  \n",
       "10  1.539867   11  0.343519  1.811751  0.210938  \n",
       "11  1.517122   10  0.216303  1.732032  0.101866  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr = 0.94      #heat removal factor\n",
    "model = 0       #from solar_df\n",
    "tref = 100      #reference temperature at 100 degC\n",
    "storage = 75        #solar water storage\n",
    "thot = 60           #required hot water\n",
    "\n",
    "calcdf['mwt'] = demand['mwt']\n",
    "\n",
    "# Calculate X\n",
    "calcdf['X'] = irradiance_area*fr*solar_df.loc[model, 'k1']*(tref - calcdf['outdoor_temperature'])*calcdf['calendar_days']*24*3600/calcdf['L']\n",
    "calcdf[\"X'\"] = calcdf['X']*(11.6 + 1.18*thot + 3.86*calcdf['mwt'] - 2.32*calcdf['outdoor_temperature'])/(100 - calcdf['outdoor_temperature'])\n",
    "\n",
    "#calculate Y\n",
    "calcdf['Y'] = irradiance_area*fr*solar_df.loc[model, 'tau_alpha']*calcdf['H']*calcdf['calendar_days']/calcdf['L']\n",
    "\n",
    "\n",
    "calcdf['f'] = 1.029*calcdf['Y'] - 0.065*calcdf[\"X'\"] - 0.245*(calcdf['Y']**2) + 0.0018*calcdf[\"X'\"]  + 0.0215*(calcdf['Y']**3)\n",
    "\n",
    "calcdf\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "492c214e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Months'), Text(0, 0.5, 'Solar Fraction')]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x252 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style='whitegrid')\n",
    "ax = sns.barplot(x='month', y='f', data=calcdf)\n",
    "ax.set(xlabel='Months', ylabel='Solar Fraction')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "010c060195082a62cdd6b8098f0e2723bcd067d7919f5c757e63a3a49d583cac"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}