2-Simulations_hourly-3years.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from eppy import modeleditor\n",
    "from eppy.modeleditor import IDF\n",
    "from matplotlib import pyplot as plt\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from besos import eppy_funcs as ef\n",
    "from besos import sampling\n",
    "from besos.evaluator import EvaluatorEP\n",
    "from besos.parameters import RangeParameter, FieldSelector, FilterSelector, Parameter, expand_plist, wwr, CategoryParameter, GenericSelector\n",
    "from besos.problem import EPProblem\n",
    "import matplotlib.pyplot as plt\n",
    "output_path=\"./output/simulation_hourly_3years/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "iddfile='./energyplus/EnergyPlus-9-0-1/Energy+.idd'\n",
    "fname = './input/on_double.idf'\n",
    "weather='./input/ITA_Torino_160590_IWEC.epw'\n",
    "IDF.setiddname(iddfile)\n",
    "idf = IDF(fname,weather)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total one area = 1084.534275140951\n"
     ]
    }
   ],
   "source": [
    "zones = idf.idfobjects[\"ZONE\"]\n",
    "total_area=0\n",
    "for i in range(len(zones)):\n",
    "    zone = zones[i]\n",
    "    area = modeleditor.zonearea(idf, zone.Name)\n",
    "    total_area=total_area+area\n",
    "\n",
    "print(\"total one area = %s\" % (total_area, ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Orientation</th>\n",
       "      <th>Insulation Thickness</th>\n",
       "      <th>Window to Wall Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>270</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Orientation  Insulation Thickness  Window to Wall Ratio\n",
       "0          270                  0.35                  0.15"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples_temp =[]\n",
    "samples_temp.append({'Orientation': 270,\n",
    "                     'Insulation Thickness': 0.35,\n",
    "                     'Window to Wall Ratio': 0.15,})\n",
    "samples = pd.DataFrame.from_dict(samples_temp)\n",
    "samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "building = ef.get_building(fname)\n",
    "insulation = FieldSelector(class_name='Material', object_name='MW Glass Wool (rolls)_O.1445', \n",
    "                           field_name='Thickness')\n",
    "insulationPR=Parameter(selector=insulation,name='Insulation Thickness')\n",
    "\n",
    "window_to_wall = wwr(CategoryParameter([0.15])) \n",
    "\n",
    "orientation = FieldSelector(class_name='Building', field_name='North Axis')\n",
    "orientationPR = Parameter(selector=orientation, value_descriptor=CategoryParameter(options=[270]),\n",
    "                          name='Orientation')\n",
    "\n",
    "parameters = [orientationPR , window_to_wall, insulationPR]\n",
    "objectives = ['Electricity:Facility','DistrictHeating:Facility','DistrictCooling:Facility'] # these get made into `MeterReader` or `VariableReader`\n",
    "problem=EPProblem(parameters, objectives) # problem = parameters + objectives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluator = EvaluatorEP(problem, building, out_dir=output_path, err_dir=output_path, epw=weather) # evaluator = problem + building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#hourly simulation\n",
    "for i in range(len(idf.idfobjects['OUTPUT:VARIABLE'])):\n",
    "    idf.idfobjects['OUTPUT:VARIABLE'][i].Reporting_Frequency='hourly'\n",
    "for i in range(len(idf.idfobjects['OUTPUT:METER'])):\n",
    "    idf.idfobjects['OUTPUT:METER'][i].Reporting_Frequency='hourly'\n",
    "idf.idfobjects['OUTPUT:ENVIRONMENTALIMPACTFACTORS'][0].Reporting_Frequency='hourly'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Electricity:Facility  ::  50169.43 kWh\n",
      "DistrictHeating:Facility  ::  37120.58 kWh\n",
      "DistrictCooling:Facility  ::  7722.42 kWh\n"
     ]
    }
   ],
   "source": [
    "#Now we run the evaluator with the given parameters\n",
    "result = evaluator([270,0.15,0.35]) \n",
    "values = dict(zip(objectives, result))\n",
    "for key, value in values.items():\n",
    "    print(key, \" :: \", \"{0:.2f}\".format(value/3.6e6), \"kWh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "/home/ict4bd_v2/Desktop/ICT-for-Building-Design-main/energyplus/EnergyPlus-9-0-1/energyplus --weather /home/ict4bd_v2/Desktop/ICT-for-Building-Design-main/input/ITA_Torino_160590_IWEC.epw --output-directory /home/ict4bd_v2/Desktop/ICT-for-Building-Design-main/output/simulation_hourly_3years --annual --idd ./energyplus/EnergyPlus-9-0-1/Energy+.idd --readvars /home/ict4bd_v2/Desktop/ICT-for-Building-Design-main/in.idf\r\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#simulation 2019\n",
    "run_period = idf.idfobjects['RunPeriod'][0]\n",
    "run_period.Begin_Year = 2017\n",
    "run_period.End_Year = 2019\n",
    "run_period\n",
    "idf.idfobjects['RunPeriod'][0]\n",
    "idf.run(readvars=True,output_directory=output_path,annual= True)\n",
    "idf_data =pd.read_csv(output_path+'eplusout.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Date/Time',\n",
       " 'Environment:Site Outdoor Air Drybulb Temperature [C](Hourly)',\n",
       " 'BLOCK1:ZONE3:Zone Operative Temperature [C](Hourly)',\n",
       " 'BLOCK1:ZONE1:Zone Operative Temperature [C](Hourly)',\n",
       " 'BLOCK1:ZONE4:Zone Operative Temperature [C](Hourly)',\n",
       " 'BLOCK1:ZONE2:Zone Operative Temperature [C](Hourly)',\n",
       " 'Electricity:Facility [J](Hourly)',\n",
       " 'DistrictCooling:Facility [J](Hourly)',\n",
       " 'DistrictHeating:Facility [J](Hourly)']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting required columns \n",
    "columns=['Date/Time']\n",
    "for i in idf_data.columns:\n",
    "    if ('Zone Operative Temperature' in i or 'District' in i or 'Electricity:Facility' in i or 'Drybulb' in i) and ('Hourly' in i): \n",
    "        columns=columns+[i]\n",
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_columns = {'Date/Time':'Date_Time',\n",
    "              'Environment:Site Outdoor Air Drybulb Temperature [C](Hourly)':'t_out',\n",
    "              'BLOCK1:ZONE3:Zone Operative Temperature [C](Hourly)':'t_in_ZONE3',\n",
    "              'BLOCK1:ZONE1:Zone Operative Temperature [C](Hourly)':'t_in_ZONE1',\n",
    "              'BLOCK1:ZONE4:Zone Operative Temperature [C](Hourly)':'t_in_ZONE4',\n",
    "              'BLOCK1:ZONE2:Zone Operative Temperature [C](Hourly)':'t_in_ZONE2',\n",
    "              'DistrictHeating:Facility [J](Hourly)':'power_heating',\n",
    "              'DistrictCooling:Facility [J](Hourly)':'power_cooling' ,\n",
    "              'Electricity:Facility [J](Hourly)':'power_electricity'}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_Time</th>\n",
       "      <th>t_out</th>\n",
       "      <th>t_in_ZONE3</th>\n",
       "      <th>t_in_ZONE1</th>\n",
       "      <th>t_in_ZONE4</th>\n",
       "      <th>t_in_ZONE2</th>\n",
       "      <th>power_electricity</th>\n",
       "      <th>power_cooling</th>\n",
       "      <th>power_heating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01  01:00:00</td>\n",
       "      <td>0.925</td>\n",
       "      <td>13.881985</td>\n",
       "      <td>14.704189</td>\n",
       "      <td>13.783859</td>\n",
       "      <td>13.845197</td>\n",
       "      <td>2.198661e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/01  02:00:00</td>\n",
       "      <td>0.650</td>\n",
       "      <td>13.407667</td>\n",
       "      <td>13.970457</td>\n",
       "      <td>13.264381</td>\n",
       "      <td>13.349967</td>\n",
       "      <td>2.198661e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/01  03:00:00</td>\n",
       "      <td>0.850</td>\n",
       "      <td>13.093317</td>\n",
       "      <td>13.557124</td>\n",
       "      <td>12.962577</td>\n",
       "      <td>13.026059</td>\n",
       "      <td>2.198661e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.990230e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/01  04:00:00</td>\n",
       "      <td>0.975</td>\n",
       "      <td>12.840838</td>\n",
       "      <td>13.204021</td>\n",
       "      <td>12.814173</td>\n",
       "      <td>12.830734</td>\n",
       "      <td>2.198661e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.177752e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/01  05:00:00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>12.716023</td>\n",
       "      <td>12.894248</td>\n",
       "      <td>12.702670</td>\n",
       "      <td>12.713656</td>\n",
       "      <td>2.198661e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.533797e+06</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26275</th>\n",
       "      <td>12/31  20:00:00</td>\n",
       "      <td>0.875</td>\n",
       "      <td>16.786543</td>\n",
       "      <td>16.590183</td>\n",
       "      <td>16.868302</td>\n",
       "      <td>16.838434</td>\n",
       "      <td>4.147716e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.934283e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26276</th>\n",
       "      <td>12/31  21:00:00</td>\n",
       "      <td>0.575</td>\n",
       "      <td>16.853741</td>\n",
       "      <td>16.805404</td>\n",
       "      <td>16.932544</td>\n",
       "      <td>16.905977</td>\n",
       "      <td>4.147716e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.663718e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26277</th>\n",
       "      <td>12/31  22:00:00</td>\n",
       "      <td>0.275</td>\n",
       "      <td>16.903341</td>\n",
       "      <td>16.942140</td>\n",
       "      <td>16.978954</td>\n",
       "      <td>16.955608</td>\n",
       "      <td>4.147716e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.420745e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26278</th>\n",
       "      <td>12/31  23:00:00</td>\n",
       "      <td>0.200</td>\n",
       "      <td>16.938986</td>\n",
       "      <td>17.035979</td>\n",
       "      <td>17.011243</td>\n",
       "      <td>16.990716</td>\n",
       "      <td>4.147716e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.011249e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26279</th>\n",
       "      <td>12/31  24:00:00</td>\n",
       "      <td>0.275</td>\n",
       "      <td>16.957496</td>\n",
       "      <td>15.233532</td>\n",
       "      <td>17.025780</td>\n",
       "      <td>17.007323</td>\n",
       "      <td>1.764454e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.222614e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26280 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "              Date_Time  t_out  t_in_ZONE3  t_in_ZONE1  t_in_ZONE4  \\\n",
       "0       01/01  01:00:00  0.925   13.881985   14.704189   13.783859   \n",
       "1       01/01  02:00:00  0.650   13.407667   13.970457   13.264381   \n",
       "2       01/01  03:00:00  0.850   13.093317   13.557124   12.962577   \n",
       "3       01/01  04:00:00  0.975   12.840838   13.204021   12.814173   \n",
       "4       01/01  05:00:00  1.000   12.716023   12.894248   12.702670   \n",
       "...                 ...    ...         ...         ...         ...   \n",
       "26275   12/31  20:00:00  0.875   16.786543   16.590183   16.868302   \n",
       "26276   12/31  21:00:00  0.575   16.853741   16.805404   16.932544   \n",
       "26277   12/31  22:00:00  0.275   16.903341   16.942140   16.978954   \n",
       "26278   12/31  23:00:00  0.200   16.938986   17.035979   17.011243   \n",
       "26279   12/31  24:00:00  0.275   16.957496   15.233532   17.025780   \n",
       "\n",
       "       t_in_ZONE2  power_electricity  power_cooling  power_heating  \n",
       "0       13.845197       2.198661e+06            0.0   0.000000e+00  \n",
       "1       13.349967       2.198661e+06            0.0   0.000000e+00  \n",
       "2       13.026059       2.198661e+06            0.0   7.990230e+05  \n",
       "3       12.830734       2.198661e+06            0.0   4.177752e+06  \n",
       "4       12.713656       2.198661e+06            0.0   7.533797e+06  \n",
       "...           ...                ...            ...            ...  \n",
       "26275   16.838434       4.147716e+07            0.0   8.934283e+07  \n",
       "26276   16.905977       4.147716e+07            0.0   8.663718e+07  \n",
       "26277   16.955608       4.147716e+07            0.0   8.420745e+07  \n",
       "26278   16.990716       4.147716e+07            0.0   8.011249e+07  \n",
       "26279   17.007323       1.764454e+07            0.0   6.222614e+07  \n",
       "\n",
       "[26280 rows x 9 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idf_data=idf_data[columns]\n",
    "data = idf_data.rename(columns =df_columns)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['t_in'] = data[['t_in_ZONE3','t_in_ZONE1','t_in_ZONE4', 't_in_ZONE2']].mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_Time</th>\n",
       "      <th>t_out</th>\n",
       "      <th>power_electricity</th>\n",
       "      <th>power_cooling</th>\n",
       "      <th>power_heating</th>\n",
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       "      <th>0</th>\n",
       "      <td>01/01  01:00:00</td>\n",
       "      <td>0.925</td>\n",
       "      <td>2.198661e+06</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>14.053807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/01  02:00:00</td>\n",
       "      <td>0.650</td>\n",
       "      <td>2.198661e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>13.498118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/01  03:00:00</td>\n",
       "      <td>0.850</td>\n",
       "      <td>2.198661e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.990230e+05</td>\n",
       "      <td>13.159769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/01  04:00:00</td>\n",
       "      <td>0.975</td>\n",
       "      <td>2.198661e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.177752e+06</td>\n",
       "      <td>12.922441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/01  05:00:00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.198661e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.533797e+06</td>\n",
       "      <td>12.756649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26275</th>\n",
       "      <td>12/31  20:00:00</td>\n",
       "      <td>0.875</td>\n",
       "      <td>4.147716e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.934283e+07</td>\n",
       "      <td>16.770866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26276</th>\n",
       "      <td>12/31  21:00:00</td>\n",
       "      <td>0.575</td>\n",
       "      <td>4.147716e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.663718e+07</td>\n",
       "      <td>16.874417</td>\n",
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       "    <tr>\n",
       "      <th>26277</th>\n",
       "      <td>12/31  22:00:00</td>\n",
       "      <td>0.275</td>\n",
       "      <td>4.147716e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.420745e+07</td>\n",
       "      <td>16.945011</td>\n",
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       "    <tr>\n",
       "      <th>26278</th>\n",
       "      <td>12/31  23:00:00</td>\n",
       "      <td>0.200</td>\n",
       "      <td>4.147716e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.011249e+07</td>\n",
       "      <td>16.994231</td>\n",
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       "    <tr>\n",
       "      <th>26279</th>\n",
       "      <td>12/31  24:00:00</td>\n",
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       "      <td>1.764454e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.222614e+07</td>\n",
       "      <td>16.556033</td>\n",
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       "<p>26280 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "              Date_Time  t_out  power_electricity  power_cooling  \\\n",
       "0       01/01  01:00:00  0.925       2.198661e+06            0.0   \n",
       "1       01/01  02:00:00  0.650       2.198661e+06            0.0   \n",
       "2       01/01  03:00:00  0.850       2.198661e+06            0.0   \n",
       "3       01/01  04:00:00  0.975       2.198661e+06            0.0   \n",
       "4       01/01  05:00:00  1.000       2.198661e+06            0.0   \n",
       "...                 ...    ...                ...            ...   \n",
       "26275   12/31  20:00:00  0.875       4.147716e+07            0.0   \n",
       "26276   12/31  21:00:00  0.575       4.147716e+07            0.0   \n",
       "26277   12/31  22:00:00  0.275       4.147716e+07            0.0   \n",
       "26278   12/31  23:00:00  0.200       4.147716e+07            0.0   \n",
       "26279   12/31  24:00:00  0.275       1.764454e+07            0.0   \n",
       "\n",
       "       power_heating       t_in  \n",
       "0       0.000000e+00  14.053807  \n",
       "1       0.000000e+00  13.498118  \n",
       "2       7.990230e+05  13.159769  \n",
       "3       4.177752e+06  12.922441  \n",
       "4       7.533797e+06  12.756649  \n",
       "...              ...        ...  \n",
       "26275   8.934283e+07  16.770866  \n",
       "26276   8.663718e+07  16.874417  \n",
       "26277   8.420745e+07  16.945011  \n",
       "26278   8.011249e+07  16.994231  \n",
       "26279   6.222614e+07  16.556033  \n",
       "\n",
       "[26280 rows x 6 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.drop(['t_in_ZONE3', 't_in_ZONE1', 't_in_ZONE4','t_in_ZONE2'], axis = 1)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
       "      <td>2017/01/01  05:00:00</td>\n",
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       "      <td>2.198661e+06</td>\n",
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       "      <td>0.575</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>8.663718e+07</td>\n",
       "      <td>16.874417</td>\n",
       "      <td>16.299417</td>\n",
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       "      <th>26277</th>\n",
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       "      <td>0.275</td>\n",
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      "text/plain": [
       "                  Date_Time  t_out  power_electricity  power_cooling  \\\n",
       "0      2017/01/01  01:00:00  0.925       2.198661e+06            0.0   \n",
       "1      2017/01/01  02:00:00  0.650       2.198661e+06            0.0   \n",
       "2      2017/01/01  03:00:00  0.850       2.198661e+06            0.0   \n",
       "3      2017/01/01  04:00:00  0.975       2.198661e+06            0.0   \n",
       "4      2017/01/01  05:00:00  1.000       2.198661e+06            0.0   \n",
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       "26279  2019/12/31  23:59:59  0.275       1.764454e+07            0.0   \n",
       "\n",
       "       power_heating       t_in  temp_diff  \n",
       "0       0.000000e+00  14.053807  13.128807  \n",
       "1       0.000000e+00  13.498118  12.848118  \n",
       "2       7.990230e+05  13.159769  12.309769  \n",
       "3       4.177752e+06  12.922441  11.947441  \n",
       "4       7.533797e+06  12.756649  11.756649  \n",
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       "26276   8.663718e+07  16.874417  16.299417  \n",
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       "26278   8.011249e+07  16.994231  16.794231  \n",
       "26279   6.222614e+07  16.556033  16.281033  \n",
       "\n",
       "[26280 rows x 7 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['temp_diff'] =data['t_in'] - data['t_out']\n",
    "\n",
    "first_year = data[0:8760]\n",
    "first_year['Date_Time'] = '2017/' + first_year['Date_Time'].str.strip()\n",
    "second_year = data[8760:17520]\n",
    "second_year['Date_Time'] = '2018/' + second_year['Date_Time'].str.strip()\n",
    "third_year = data[17520:]\n",
    "third_year['Date_Time'] = '2019/' + third_year['Date_Time'].str.strip()\n",
    "\n",
    "data['Date_Time'] = data['Date_Time'].str.replace('24:00:00','23:59:59')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting from J to kWh\n",
    "data['power_heating'] /= 3.6e6\n",
    "data['power_cooling'] /= 3.6e6\n",
    "data['power_electricity'] /= 3.6e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>26276</th>\n",
       "      <td>2019/12/31  21:00:00</td>\n",
       "      <td>0.575</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>24.065884</td>\n",
       "      <td>16.874417</td>\n",
       "      <td>16.299417</td>\n",
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       "    <tr>\n",
       "      <th>26277</th>\n",
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       "      <td>0.0</td>\n",
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      "text/plain": [
       "                  Date_Time  t_out  power_electricity  power_cooling  \\\n",
       "0      2017/01/01  01:00:00  0.925           0.610739            0.0   \n",
       "1      2017/01/01  02:00:00  0.650           0.610739            0.0   \n",
       "2      2017/01/01  03:00:00  0.850           0.610739            0.0   \n",
       "3      2017/01/01  04:00:00  0.975           0.610739            0.0   \n",
       "4      2017/01/01  05:00:00  1.000           0.610739            0.0   \n",
       "...                     ...    ...                ...            ...   \n",
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       "26279  2019/12/31  23:59:59  0.275           4.901261            0.0   \n",
       "\n",
       "       power_heating       t_in  temp_diff  \n",
       "0           0.000000  14.053807  13.128807  \n",
       "1           0.000000  13.498118  12.848118  \n",
       "2           0.221951  13.159769  12.309769  \n",
       "3           1.160487  12.922441  11.947441  \n",
       "4           2.092721  12.756649  11.756649  \n",
       "...              ...        ...        ...  \n",
       "26275      24.817454  16.770866  15.895866  \n",
       "26276      24.065884  16.874417  16.299417  \n",
       "26277      23.390958  16.945011  16.670011  \n",
       "26278      22.253470  16.994231  16.794231  \n",
       "26279      17.285040  16.556033  16.281033  \n",
       "\n",
       "[26280 rows x 7 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['total_power'] = data['power_heating']+ data['power_cooling']\n",
    "data = data[['Date_Time','t_in','t_out','temp_diff','power_heating','power_cooling','power_electricity','total_power']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv(path_or_buf=output_path+'simulation_data_3years_hourly.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>Date_Time</th>\n",
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       "      <th>26276</th>\n",
       "      <td>2019/12/31  21:00:00</td>\n",
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       "      <td>16.299417</td>\n",
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      "text/plain": [
       "                  Date_Time       t_in  t_out  temp_diff  power_heating  \\\n",
       "0      2017/01/01  01:00:00  14.053807  0.925  13.128807       0.000000   \n",
       "1      2017/01/01  02:00:00  13.498118  0.650  12.848118       0.000000   \n",
       "2      2017/01/01  03:00:00  13.159769  0.850  12.309769       0.221951   \n",
       "3      2017/01/01  04:00:00  12.922441  0.975  11.947441       1.160487   \n",
       "4      2017/01/01  05:00:00  12.756649  1.000  11.756649       2.092721   \n",
       "...                     ...        ...    ...        ...            ...   \n",
       "26275  2019/12/31  20:00:00  16.770866  0.875  15.895866      24.817454   \n",
       "26276  2019/12/31  21:00:00  16.874417  0.575  16.299417      24.065884   \n",
       "26277  2019/12/31  22:00:00  16.945011  0.275  16.670011      23.390958   \n",
       "26278  2019/12/31  23:00:00  16.994231  0.200  16.794231      22.253470   \n",
       "26279  2019/12/31  23:59:59  16.556033  0.275  16.281033      17.285040   \n",
       "\n",
       "       power_cooling  power_electricity  total_power  \n",
       "0                0.0           0.610739     0.000000  \n",
       "1                0.0           0.610739     0.000000  \n",
       "2                0.0           0.610739     0.221951  \n",
       "3                0.0           0.610739     1.160487  \n",
       "4                0.0           0.610739     2.092721  \n",
       "...              ...                ...          ...  \n",
       "26275            0.0          11.521435    24.817454  \n",
       "26276            0.0          11.521435    24.065884  \n",
       "26277            0.0          11.521435    23.390958  \n",
       "26278            0.0          11.521435    22.253470  \n",
       "26279            0.0           4.901261    17.285040  \n",
       "\n",
       "[26280 rows x 8 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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