trained lstm on bigger set

EnergyLSTM/EDA_lstm_energy.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "metadata": {
     "vscode": {
      "languageId": "ruby"
@@ -103,11 +103,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 63,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1d98c8cdfd0>]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "eed_1h[eed_1h['hvac_S'].isna()]"
+    "plt.plot(eed_1h['hvac_N'])"
    ]
   },
   {
@@ -134,11 +145,11 @@
     "    secondTSr.index = indexr[:len(secondTSr)]\n",
     "    \n",
     "    #FORWARD    \n",
-    "    es = ExponentialSmoothing(firstTS,  seasonal_periods=seasonal_periods,seasonal='add').fit()\n",
+    "    es = ExponentialSmoothing(firstTS,  seasonal_periods=seasonal_periods,seasonal='add', freq='H').fit()\n",
     "    forwardPrediction = es.predict(start=firstTS.index[-1]+one, end=secondTS.index[0]-one)\n",
     "    \n",
     "    #BACKWARD\n",
-    "    es = ExponentialSmoothing(secondTSr,  seasonal_periods=seasonal_periods,seasonal='add').fit()\n",
+    "    es = ExponentialSmoothing(secondTSr,  seasonal_periods=seasonal_periods,seasonal='add', freq='H').fit()\n",
     "    backwardPrediction = es.predict(start=secondTSr.index[-1]+one, end=firstTSr.index[0]-one)\n",
     "    \n",
     "    #INTERPOLATION\n",
@@ -150,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +171,7 @@
     "    # Prepare the DataFrame\n",
     "    df = data.copy()\n",
     "    df = df.reset_index()\n",
-    "    df= df.dropna()\n",
+    "    df = df.dropna()\n",
     "    \n",
     "    # Set the maximum allowable gap (e.g., 1 hour)\n",
     "    max_gap = pd.Timedelta(hours=1)\n",
@@ -184,115 +195,191 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
-    "seasonal_periods = 24\n",
-    "dfs = split_dfs(eed_1h[['hvac_N']])\n",
+    "def interpolate_gaps(data, col):\n",
+    "\n",
+    "    # Split the data into multiple DataFrames based on the gaps\n",
+    "    dfs = split_dfs(data[[col]])\n",
+    "\n",
+    "    # Interpolate the gaps between the DataFrames\n",
+    "    ip_df = pd.DataFrame()\n",
+    "    for ii in range(len(dfs)-1):\n",
+    "        seasonal_periods = max(min([len(dfs[ii]), len(dfs[ii+1])]) // 2 -  10, 2)\n",
+    "        \n",
+    "        if seasonal_periods > 2*24*7 + 10: # Using more than 1 week of seasonal patterns is not necessary\n",
+    "            seasonal_periods = 24*7\n",
+    "            interpolation = fillgap(dfs[ii][col], dfs[ii+1][col], seasonal_periods)\n",
+    "        else:\n",
+    "            interpolation = fillgap(dfs[ii][col], dfs[ii+1][col], seasonal_periods)\n",
     "\n",
-    "# Interpolate the gaps between the DataFrames\n",
-    "ip_df = pd.DataFrame()\n",
-    "for ii in range(len(dfs)-1):\n",
-    "    if (len(dfs[ii]) > 2*seasonal_periods+10) and (len(dfs[ii+1]) > 2*seasonal_periods+10):\n",
-    "        interpolation = fillgap(dfs[ii]['hvac_N'], dfs[ii+1]['hvac_N'], seasonal_periods)\n",
-    "        ip_df = pd.concat([ip_df,interpolation])\n",
-    "    else:\n",
-    "        continue"
+    "        ip_df = pd.concat([ip_df,dfs[ii][col],interpolation])\n",
+    "    \n",
+    "    # Add the last DataFrame\n",
+    "    ip_df = pd.concat([ip_df,dfs[-1][col]])\n",
+    "\n",
+    "    return ip_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
       "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
       "  warnings.warn(\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
       "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
       "  warnings.warn(\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
       "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
       "  warnings.warn(\n",
-      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency H will be used.\n",
-      "  self._init_dates(dates, freq)\n",
+      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
+      "  warnings.warn(\n",
       "c:\\Users\\levim\\anaconda3\\envs\\experiments\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
       "  warnings.warn(\n"
      ]
+    },
+    {
+     "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>hvac_N</th>\n",
+       "      <th>hvac_S</th>\n",
+       "      <th>air_temp_set_1</th>\n",
+       "      <th>solar_radiation_set_1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 01:00:00</th>\n",
+       "      <td>37.525001</td>\n",
+       "      <td>19.395</td>\n",
+       "      <td>10.8900</td>\n",
+       "      <td>2.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 02:00:00</th>\n",
+       "      <td>37.750001</td>\n",
+       "      <td>22.775</td>\n",
+       "      <td>10.7550</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 03:00:00</th>\n",
+       "      <td>37.550001</td>\n",
+       "      <td>18.920</td>\n",
+       "      <td>10.4775</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 04:00:00</th>\n",
+       "      <td>36.675001</td>\n",
+       "      <td>21.600</td>\n",
+       "      <td>9.9925</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 05:00:00</th>\n",
+       "      <td>37.272500</td>\n",
+       "      <td>19.000</td>\n",
+       "      <td>9.8050</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        hvac_N  hvac_S  air_temp_set_1  solar_radiation_set_1\n",
+       "2018-01-01 01:00:00  37.525001  19.395         10.8900                  2.125\n",
+       "2018-01-01 02:00:00  37.750001  22.775         10.7550                  0.000\n",
+       "2018-01-01 03:00:00  37.550001  18.920         10.4775                  0.000\n",
+       "2018-01-01 04:00:00  36.675001  21.600          9.9925                  0.000\n",
+       "2018-01-01 05:00:00  37.272500  19.000          9.8050                  0.000"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "seasonal_periods = 24\n",
-    "dfs = split_dfs(eed_1h[['hvac_N']])\n",
+    "# interpolation of the whole data set\n",
     "\n",
-    "# Interpolate the gaps between the DataFrames\n",
-    "ip_df = pd.DataFrame()\n",
-    "for ii in range(len(dfs)-1):\n",
-    "    seasonal_periods = max(min([len(dfs[ii]), len(dfs[ii+1])]) // 2 -  10, 2)\n",
-    "    \n",
-    "    if seasonal_periods > 2*24*7 + 10: # Using more than 1 week of seasonal patterns is not necessary\n",
-    "        seasonal_periods = 24*7\n",
-    "        interpolation = fillgap(dfs[ii]['hvac_N'], dfs[ii+1]['hvac_N'], seasonal_periods)\n",
-    "    else:\n",
-    "        interpolation = fillgap(dfs[ii]['hvac_N'], dfs[ii+1]['hvac_N'], seasonal_periods)\n",
+    "ip_eed_1h = pd.DataFrame()\n",
+    "for ii in eed_1h.columns:\n",
+    "    ip_df = interpolate_gaps(eed_1h, ii)\n",
+    "    ip_eed_1h = pd.concat([ip_eed_1h, ip_df[0]], axis=1)  # axis=1 for horizontal concat\n",
+    "ip_eed_1h.columns = list(eed_1h.columns)\n",
+    "\n",
+    "# Reset the index and rename the columns\n",
+    "ip_eed_1h = ip_eed_1h.reset_index()\n",
+    "ip_eed_1h = ip_eed_1h.rename(columns={'index': 'date'})\n",
+    "ip_eed_1h.head()\n",
+    "\n",
+    "ip_eed_1h.to_csv(dataPATH + r\"\\interpolated_energy_data.csv\")\n",
     "\n",
-    "    ip_df = pd.concat([ip_df,interpolation])\n"
+    "ip_eed_1h.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib qt\n",
-    "plt.plot(eed_1h['hvac_N'])\n",
+    "# plt.plot(eed_1h['hvac_N'])\n",
     "plt.plot(ip_df)\n",
     "\n",
     "plt.show()"
@@ -300,14 +387,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
-    "seasonal_periods=2\n",
-    "for ii in range(len(dfs)-1):\n",
-    "    interpolation = fillgap(dfs[ii]['hvac_N'], dfs[ii+1]['hvac_N'], seasonal_periods)\n",
-    "    ip_df = pd.concat([ip_df,interpolation])"
+    "# Reset the index and rename the columns\n",
+    "# ip_eed_1h = ip_eed_1h.set_index('date')\n",
+    "ip_eed_1h.head()\n",
+    "\n",
+    "ip_eed_1h.to_csv(dataPATH + r\"\\interpolated_energy_data.csv\")"
    ]
   },
   {

EnergyLSTM/lstm_energy.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10,7 +10,6 @@
     "from datetime import datetime \n",
     "from datetime import date\n",
     "import matplotlib.pyplot as plt\n",
-    "# import seaborn as sns\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "from keras.models import Sequential\n",
@@ -21,12 +20,45 @@
     "\n",
     "dataPATH = r\"C:\\Users\\levim\\OneDrive\\Documents\\MastersAI_ES\\TeamProject-5ARIP10\\smart-buildings\\Data\"\n",
     "# all_data = pd.read_csv(dataPATH + r\"\\long_merge.csv\")\n",
-    "all_data = pd.read_csv(dataPATH + r\"\\extended_energy_data.csv\")"
+    "all_data = pd.read_csv(dataPATH + r\"\\extended_energy_data.csv\")\n",
+    "interpolated_data = pd.read_csv(dataPATH + r\"\\interpolated_energy_data.csv\", index_col=0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prepar energy data set with extended features\n",
+    "feature_list = ['date', 'hvac_N', 'hvac_S', 'air_temp_set_1', 'solar_radiation_set_1']\n",
+    "extended_energy_data = all_data[feature_list]\n",
+    "\n",
+    "extended_energy_data['date'] = pd.to_datetime(extended_energy_data['date'])\n",
+    "extended_energy_data.set_index('date', inplace=True)\n",
+    "\n",
+    "eed_15m = extended_energy_data.resample('15T').mean()\n",
+    "eed_1h = extended_energy_data.resample('60T').mean()\n",
+    "\n",
+    "eed_15m = eed_15m.reset_index(drop=False)\n",
+    "eed_1h = eed_1h.reset_index(drop=False)\n",
+    "\n",
+    "window_size = 4*4 # 4 hours\n",
+    "eed_15m_avg = eed_15m.copy()\n",
+    "eed_15m_avg['hvac_N'] = eed_15m['hvac_N'].rolling(window=window_size).mean()\n",
+    "eed_15m_avg['hvac_S'] = eed_15m['hvac_S'].rolling(window=window_size).mean()\n",
+    "\n",
+    "window_size = 4 # 4 hours\n",
+    "eed_1h_avg = eed_1h.copy()\n",
+    "eed_1h_avg['hvac_N'] = eed_1h['hvac_N'].rolling(window=window_size).mean()\n",
+    "eed_1h_avg['hvac_S'] = eed_1h['hvac_S'].rolling(window=window_size).mean()\n",
+    "\n",
+    "eed_15m.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -53,128 +85,106 @@
        "      <th>date</th>\n",
        "      <th>hvac_N</th>\n",
        "      <th>hvac_S</th>\n",
+       "      <th>day_of_week</th>\n",
        "      <th>air_temp_set_1</th>\n",
        "      <th>solar_radiation_set_1</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>11.64</td>\n",
-       "      <td>86.70</td>\n",
+       "      <th>23</th>\n",
+       "      <td>2018-01-02 00:00:00</td>\n",
+       "      <td>38.225000</td>\n",
+       "      <td>26.4000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>14.9550</td>\n",
+       "      <td>87.4450</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2018-01-01 00:15:00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>11.49</td>\n",
-       "      <td>45.88</td>\n",
+       "      <th>24</th>\n",
+       "      <td>2018-01-02 01:00:00</td>\n",
+       "      <td>38.297501</td>\n",
+       "      <td>21.1750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>14.2125</td>\n",
+       "      <td>2.8675</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2018-01-01 00:30:00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>11.59</td>\n",
-       "      <td>51.62</td>\n",
+       "      <th>25</th>\n",
+       "      <td>2018-01-02 02:00:00</td>\n",
+       "      <td>38.072500</td>\n",
+       "      <td>21.7225</td>\n",
+       "      <td>1</td>\n",
+       "      <td>14.2700</td>\n",
+       "      <td>0.0925</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2018-01-01 00:45:00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>11.44</td>\n",
-       "      <td>21.43</td>\n",
+       "      <th>26</th>\n",
+       "      <td>2018-01-02 03:00:00</td>\n",
+       "      <td>39.147500</td>\n",
+       "      <td>21.7000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>14.1375</td>\n",
+       "      <td>0.1175</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2018-01-01 01:00:00</td>\n",
-       "      <td>37.400002</td>\n",
-       "      <td>19.5</td>\n",
-       "      <td>11.12</td>\n",
-       "      <td>6.45</td>\n",
+       "      <th>27</th>\n",
+       "      <td>2018-01-02 04:00:00</td>\n",
+       "      <td>38.172500</td>\n",
+       "      <td>21.6250</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13.9850</td>\n",
+       "      <td>0.0725</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                 date     hvac_N  hvac_S  air_temp_set_1  \\\n",
-       "0 2018-01-01 00:00:00        NaN     NaN           11.64   \n",
-       "1 2018-01-01 00:15:00        NaN     NaN           11.49   \n",
-       "2 2018-01-01 00:30:00        NaN     NaN           11.59   \n",
-       "3 2018-01-01 00:45:00        NaN     NaN           11.44   \n",
-       "4 2018-01-01 01:00:00  37.400002    19.5           11.12   \n",
+       "                  date     hvac_N   hvac_S  day_of_week  air_temp_set_1  \\\n",
+       "23 2018-01-02 00:00:00  38.225000  26.4000            1         14.9550   \n",
+       "24 2018-01-02 01:00:00  38.297501  21.1750            1         14.2125   \n",
+       "25 2018-01-02 02:00:00  38.072500  21.7225            1         14.2700   \n",
+       "26 2018-01-02 03:00:00  39.147500  21.7000            1         14.1375   \n",
+       "27 2018-01-02 04:00:00  38.172500  21.6250            1         13.9850   \n",
        "\n",
-       "   solar_radiation_set_1  \n",
-       "0                  86.70  \n",
-       "1                  45.88  \n",
-       "2                  51.62  \n",
-       "3                  21.43  \n",
-       "4                   6.45  "
+       "    solar_radiation_set_1  \n",
+       "23                87.4450  \n",
+       "24                 2.8675  \n",
+       "25                 0.0925  \n",
+       "26                 0.1175  \n",
+       "27                 0.0725  "
       ]
      },
-     "execution_count": 23,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "# Prepar energy data set with extended features\n",
-    "feature_list = ['date', 'hvac_N', 'hvac_S', 'air_temp_set_1', 'solar_radiation_set_1']\n",
-    "extended_energy_data = all_data[feature_list]\n",
-    "\n",
-    "extended_energy_data['date'] = pd.to_datetime(extended_energy_data['date'])\n",
-    "extended_energy_data.set_index('date', inplace=True)\n",
-    "\n",
-    "eed_15m = extended_energy_data.resample('15T').mean()\n",
-    "eed_1h = extended_energy_data.resample('60T').mean()\n",
-    "\n",
-    "eed_15m = eed_15m.reset_index(drop=False)\n",
-    "eed_1h = eed_1h.reset_index(drop=False)\n",
-    "\n",
-    "window_size = 4*4 # 4 hours\n",
-    "eed_15m_avg = eed_15m.copy()\n",
-    "eed_15m_avg['hvac_N'] = eed_15m['hvac_N'].rolling(window=window_size).mean()\n",
-    "eed_15m_avg['hvac_S'] = eed_15m['hvac_S'].rolling(window=window_size).mean()\n",
-    "\n",
-    "window_size = 4 # 4 hours\n",
-    "eed_1h_avg = eed_1h.copy()\n",
-    "eed_1h_avg['hvac_N'] = eed_1h['hvac_N'].rolling(window=window_size).mean()\n",
-    "eed_1h_avg['hvac_S'] = eed_1h['hvac_S'].rolling(window=window_size).mean()\n",
-    "\n",
-    "eed_15m.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "# energy_data = pd.read_csv(dataPATH + r\"\\extended_energy_data.csv\")\n",
     "# energy_data = eed_15m\n",
-    "energy_data = eed_15m_avg\n",
+    "# energy_data = eed_15m_avg\n",
+    "energy_data = interpolated_data.copy()\n",
+    "energy_data = energy_data.reset_index()\n",
     "\n",
     "# Convert the date column to datetime\n",
     "energy_data['date'] = pd.to_datetime(energy_data['date'], format = \"%Y-%m-%d %H:%M:%S\")\n",
     "\n",
-    "energy_data['day_of_week'] = energy_data['date'].dt.weekday\n",
+    "energy_data.insert(3, 'day_of_week', energy_data['date'].dt.weekday)\n",
     "# Filter the data for the year 2019\n",
-    "df_filtered = energy_data[ (energy_data.date.dt.date >date(2019, 1, 20)) & (energy_data.date.dt.date< date(2019, 7, 26))]\n",
+    "df_filtered = energy_data[ (energy_data.date.dt.date >date(2018, 1, 1)) & (energy_data.date.dt.date< date(2021, 1, 1))]\n",
     "\n",
     "# Check for NA values in the DataFrame\n",
     "if df_filtered.isna().any().any():\n",
-    "    print(\"There are NA values in the DataFrame columns.\")"
+    "    print(\"There are NA values in the DataFrame columns.\")\n",
+    "\n",
+    "df_filtered.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -183,15 +193,15 @@
        "[]"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "testdataset_df = df_filtered[(df_filtered.date.dt.date <date(2019, 2, 20))]\n",
+    "testdataset_df = df_filtered[(df_filtered.date.dt.date >=date(2019, 3, 1)) & (df_filtered.date.dt.date <= date(2019, 6, 1))]\n",
     "\n",
-    "traindataset_df = df_filtered[ (df_filtered.date.dt.date >date(2019, 2, 21))]\n",
+    "traindataset_df = df_filtered[ (df_filtered.date.dt.date <date(2019, 3, 1)) | (df_filtered.date.dt.date > date(2019, 6, 1))]\n",
     "\n",
     "testdataset = testdataset_df.drop(columns=[\"date\"]).values\n",
     "\n",
@@ -203,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -220,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -245,46 +255,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/5\n",
-      "225/229 [============================>.] - ETA: 0s - loss: 0.0130\n",
-      "Epoch 1: val_loss improved from inf to 0.01885, saving model to lstm_energy_01.keras\n",
-      "229/229 [==============================] - 8s 16ms/step - loss: 0.0129 - val_loss: 0.0189\n",
-      "Epoch 2/5\n",
-      "229/229 [==============================] - ETA: 0s - loss: 0.0058\n",
-      "Epoch 2: val_loss did not improve from 0.01885\n",
-      "229/229 [==============================] - 3s 11ms/step - loss: 0.0058 - val_loss: 0.0192\n",
-      "Epoch 3/5\n",
-      "225/229 [============================>.] - ETA: 0s - loss: 0.0052\n",
-      "Epoch 3: val_loss improved from 0.01885 to 0.01818, saving model to lstm_energy_01.keras\n",
-      "229/229 [==============================] - 3s 11ms/step - loss: 0.0052 - val_loss: 0.0182\n",
-      "Epoch 4/5\n",
-      "225/229 [============================>.] - ETA: 0s - loss: 0.0045\n",
-      "Epoch 4: val_loss did not improve from 0.01818\n",
-      "229/229 [==============================] - 3s 11ms/step - loss: 0.0045 - val_loss: 0.0190\n",
-      "Epoch 5/5\n",
-      "226/229 [============================>.] - ETA: 0s - loss: 0.0041\n",
-      "Epoch 5: val_loss did not improve from 0.01818\n",
-      "229/229 [==============================] - 3s 11ms/step - loss: 0.0041 - val_loss: 0.0186\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<keras.callbacks.History at 0x25e69ac0370>"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train,test = traindataset,testdataset\n",
     "steps_in_past   = 3   \n",
@@ -318,19 +291,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4/4 [==============================] - 0s 4ms/step - loss: 0.0153\n",
-      "4/4 [==============================] - 1s 4ms/step\n",
-      "Loss:  0.01531214825809002\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "loss            = model.evaluate(X_test, y_test)\n",
     "test_predict1   = model.predict(X_test)\n",
@@ -343,21 +306,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "IndexError",
-     "evalue": "index 106 is out of bounds for axis 0 with size 106",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[52], line 10\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;66;03m# Loop over the value index\u001b[39;00m\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(axes\u001b[38;5;241m.\u001b[39mflat):\n\u001b[0;32m      9\u001b[0m     \u001b[38;5;66;03m# Plot your data or perform any other operations\u001b[39;00m\n\u001b[1;32m---> 10\u001b[0m     ax\u001b[38;5;241m.\u001b[39mplot(\u001b[43my_test\u001b[49m\u001b[43m[\u001b[49m\u001b[43mvar\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43mtime_step\u001b[49m\u001b[43m]\u001b[49m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOriginal Testing Data\u001b[39m\u001b[38;5;124m'\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblue\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     11\u001b[0m     ax\u001b[38;5;241m.\u001b[39mplot(test_predict1[var\u001b[38;5;241m+\u001b[39mi,\u001b[38;5;241m0\u001b[39m:time_step], label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPredicted Testing Data\u001b[39m\u001b[38;5;124m'\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mred\u001b[39m\u001b[38;5;124m'\u001b[39m,alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.8\u001b[39m)\n\u001b[0;32m     12\u001b[0m     \u001b[38;5;66;03m# ax.set_title(f'Plot {i+1}')\u001b[39;00m\n",
-      "\u001b[1;31mIndexError\u001b[0m: index 106 is out of bounds for axis 0 with size 106"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%matplotlib qt\n",
     "\n",
@@ -418,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
@@ -426,94 +377,94 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/20\n",
-      " 6/10 [=================>............] - ETA: 0s - loss: 0.0850 \n",
-      "Epoch 1: val_loss improved from inf to 0.07467, saving model to lstm_energy_01.keras\n",
-      "10/10 [==============================] - 7s 131ms/step - loss: 0.0791 - val_loss: 0.0747\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.1003\n",
+      "Epoch 1: val_loss improved from inf to 0.04277, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 6s 89ms/step - loss: 0.1003 - val_loss: 0.0428\n",
       "Epoch 2/20\n",
-      " 6/10 [=================>............] - ETA: 0s - loss: 0.0487\n",
-      "Epoch 2: val_loss improved from 0.07467 to 0.03484, saving model to lstm_energy_01.keras\n",
-      "10/10 [==============================] - 0s 20ms/step - loss: 0.0419 - val_loss: 0.0348\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0340\n",
+      "Epoch 2: val_loss improved from 0.04277 to 0.03142, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 17ms/step - loss: 0.0340 - val_loss: 0.0314\n",
       "Epoch 3/20\n",
-      " 6/10 [=================>............] - ETA: 0s - loss: 0.0262\n",
-      "Epoch 3: val_loss improved from 0.03484 to 0.02388, saving model to lstm_energy_01.keras\n",
-      "10/10 [==============================] - 0s 17ms/step - loss: 0.0241 - val_loss: 0.0239\n",
+      "14/16 [=========================>....] - ETA: 0s - loss: 0.0270\n",
+      "Epoch 3: val_loss improved from 0.03142 to 0.02204, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 17ms/step - loss: 0.0268 - val_loss: 0.0220\n",
       "Epoch 4/20\n",
-      " 6/10 [=================>............] - ETA: 0s - loss: 0.0180\n",
-      "Epoch 4: val_loss improved from 0.02388 to 0.02059, saving model to lstm_energy_01.keras\n",
-      "10/10 [==============================] - 0s 18ms/step - loss: 0.0174 - val_loss: 0.0206\n",
+      "15/16 [===========================>..] - ETA: 0s - loss: 0.0220\n",
+      "Epoch 4: val_loss improved from 0.02204 to 0.01482, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 15ms/step - loss: 0.0220 - val_loss: 0.0148\n",
       "Epoch 5/20\n",
-      " 6/10 [=================>............] - ETA: 0s - loss: 0.0134\n",
-      "Epoch 5: val_loss improved from 0.02059 to 0.01839, saving model to lstm_energy_01.keras\n",
-      "10/10 [==============================] - 0s 18ms/step - loss: 0.0130 - val_loss: 0.0184\n",
+      "13/16 [=======================>......] - ETA: 0s - loss: 0.0197\n",
+      "Epoch 5: val_loss improved from 0.01482 to 0.01388, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 18ms/step - loss: 0.0192 - val_loss: 0.0139\n",
       "Epoch 6/20\n",
-      " 8/10 [=======================>......] - ETA: 0s - loss: 0.0107\n",
-      "Epoch 6: val_loss did not improve from 0.01839\n",
-      "10/10 [==============================] - 0s 21ms/step - loss: 0.0106 - val_loss: 0.0255\n",
+      "14/16 [=========================>....] - ETA: 0s - loss: 0.0176\n",
+      "Epoch 6: val_loss did not improve from 0.01388\n",
+      "16/16 [==============================] - 0s 15ms/step - loss: 0.0177 - val_loss: 0.0156\n",
       "Epoch 7/20\n",
-      " 6/10 [=================>............] - ETA: 0s - loss: 0.0090\n",
-      "Epoch 7: val_loss did not improve from 0.01839\n",
-      "10/10 [==============================] - 0s 14ms/step - loss: 0.0090 - val_loss: 0.0261\n",
+      "15/16 [===========================>..] - ETA: 0s - loss: 0.0177\n",
+      "Epoch 7: val_loss improved from 0.01388 to 0.01233, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 17ms/step - loss: 0.0177 - val_loss: 0.0123\n",
       "Epoch 8/20\n",
-      "10/10 [==============================] - ETA: 0s - loss: 0.0085\n",
-      "Epoch 8: val_loss did not improve from 0.01839\n",
-      "10/10 [==============================] - 0s 18ms/step - loss: 0.0085 - val_loss: 0.0197\n",
+      "13/16 [=======================>......] - ETA: 0s - loss: 0.0172\n",
+      "Epoch 8: val_loss improved from 0.01233 to 0.01210, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 14ms/step - loss: 0.0171 - val_loss: 0.0121\n",
       "Epoch 9/20\n",
-      " 9/10 [==========================>...] - ETA: 0s - loss: 0.0074\n",
-      "Epoch 9: val_loss improved from 0.01839 to 0.01687, saving model to lstm_energy_01.keras\n",
-      "10/10 [==============================] - 0s 22ms/step - loss: 0.0074 - val_loss: 0.0169\n",
+      "13/16 [=======================>......] - ETA: 0s - loss: 0.0174\n",
+      "Epoch 9: val_loss did not improve from 0.01210\n",
+      "16/16 [==============================] - 0s 15ms/step - loss: 0.0174 - val_loss: 0.0126\n",
       "Epoch 10/20\n",
-      " 6/10 [=================>............] - ETA: 0s - loss: 0.0066\n",
-      "Epoch 10: val_loss did not improve from 0.01687\n",
-      "10/10 [==============================] - 0s 14ms/step - loss: 0.0068 - val_loss: 0.0171\n",
+      "14/16 [=========================>....] - ETA: 0s - loss: 0.0162\n",
+      "Epoch 10: val_loss did not improve from 0.01210\n",
+      "16/16 [==============================] - 0s 16ms/step - loss: 0.0165 - val_loss: 0.0138\n",
       "Epoch 11/20\n",
-      " 6/10 [=================>............] - ETA: 0s - loss: 0.0063\n",
-      "Epoch 11: val_loss did not improve from 0.01687\n",
-      "10/10 [==============================] - 0s 14ms/step - loss: 0.0061 - val_loss: 0.0191\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0164\n",
+      "Epoch 11: val_loss did not improve from 0.01210\n",
+      "16/16 [==============================] - 0s 13ms/step - loss: 0.0164 - val_loss: 0.0141\n",
       "Epoch 12/20\n",
-      " 6/10 [=================>............] - ETA: 0s - loss: 0.0057\n",
-      "Epoch 12: val_loss improved from 0.01687 to 0.01678, saving model to lstm_energy_01.keras\n",
-      "10/10 [==============================] - 0s 18ms/step - loss: 0.0057 - val_loss: 0.0168\n",
+      "14/16 [=========================>....] - ETA: 0s - loss: 0.0167\n",
+      "Epoch 12: val_loss did not improve from 0.01210\n",
+      "16/16 [==============================] - 0s 17ms/step - loss: 0.0166 - val_loss: 0.0139\n",
       "Epoch 13/20\n",
-      " 6/10 [=================>............] - ETA: 0s - loss: 0.0052\n",
-      "Epoch 13: val_loss did not improve from 0.01678\n",
-      "10/10 [==============================] - 0s 13ms/step - loss: 0.0058 - val_loss: 0.0206\n",
+      "14/16 [=========================>....] - ETA: 0s - loss: 0.0165\n",
+      "Epoch 13: val_loss did not improve from 0.01210\n",
+      "16/16 [==============================] - 0s 17ms/step - loss: 0.0162 - val_loss: 0.0137\n",
       "Epoch 14/20\n",
-      " 6/10 [=================>............] - ETA: 0s - loss: 0.0058\n",
-      "Epoch 14: val_loss improved from 0.01678 to 0.01612, saving model to lstm_energy_01.keras\n",
-      "10/10 [==============================] - 0s 20ms/step - loss: 0.0062 - val_loss: 0.0161\n",
+      "14/16 [=========================>....] - ETA: 0s - loss: 0.0158\n",
+      "Epoch 14: val_loss did not improve from 0.01210\n",
+      "16/16 [==============================] - 0s 16ms/step - loss: 0.0156 - val_loss: 0.0122\n",
       "Epoch 15/20\n",
-      " 6/10 [=================>............] - ETA: 0s - loss: 0.0061\n",
-      "Epoch 15: val_loss did not improve from 0.01612\n",
-      "10/10 [==============================] - 0s 14ms/step - loss: 0.0059 - val_loss: 0.0184\n",
+      "14/16 [=========================>....] - ETA: 0s - loss: 0.0150\n",
+      "Epoch 15: val_loss improved from 0.01210 to 0.01155, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 17ms/step - loss: 0.0153 - val_loss: 0.0116\n",
       "Epoch 16/20\n",
-      " 6/10 [=================>............] - ETA: 0s - loss: 0.0054\n",
-      "Epoch 16: val_loss improved from 0.01612 to 0.01561, saving model to lstm_energy_01.keras\n",
-      "10/10 [==============================] - 0s 17ms/step - loss: 0.0053 - val_loss: 0.0156\n",
+      "12/16 [=====================>........] - ETA: 0s - loss: 0.0157\n",
+      "Epoch 16: val_loss did not improve from 0.01155\n",
+      "16/16 [==============================] - 0s 12ms/step - loss: 0.0158 - val_loss: 0.0116\n",
       "Epoch 17/20\n",
-      " 6/10 [=================>............] - ETA: 0s - loss: 0.0046\n",
-      "Epoch 17: val_loss did not improve from 0.01561\n",
-      "10/10 [==============================] - 0s 13ms/step - loss: 0.0048 - val_loss: 0.0166\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0149\n",
+      "Epoch 17: val_loss did not improve from 0.01155\n",
+      "16/16 [==============================] - 0s 14ms/step - loss: 0.0149 - val_loss: 0.0118\n",
       "Epoch 18/20\n",
-      " 6/10 [=================>............] - ETA: 0s - loss: 0.0054\n",
-      "Epoch 18: val_loss improved from 0.01561 to 0.01503, saving model to lstm_energy_01.keras\n",
-      "10/10 [==============================] - 0s 18ms/step - loss: 0.0052 - val_loss: 0.0150\n",
+      "15/16 [===========================>..] - ETA: 0s - loss: 0.0142\n",
+      "Epoch 18: val_loss did not improve from 0.01155\n",
+      "16/16 [==============================] - 0s 15ms/step - loss: 0.0144 - val_loss: 0.0118\n",
       "Epoch 19/20\n",
-      " 6/10 [=================>............] - ETA: 0s - loss: 0.0050\n",
-      "Epoch 19: val_loss did not improve from 0.01503\n",
-      "10/10 [==============================] - 0s 13ms/step - loss: 0.0046 - val_loss: 0.0156\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0142\n",
+      "Epoch 19: val_loss improved from 0.01155 to 0.01153, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 15ms/step - loss: 0.0142 - val_loss: 0.0115\n",
       "Epoch 20/20\n",
-      " 6/10 [=================>............] - ETA: 0s - loss: 0.0045\n",
-      "Epoch 20: val_loss did not improve from 0.01503\n",
-      "10/10 [==============================] - 0s 14ms/step - loss: 0.0045 - val_loss: 0.0153\n"
+      "12/16 [=====================>........] - ETA: 0s - loss: 0.0147\n",
+      "Epoch 20: val_loss did not improve from 0.01153\n",
+      "16/16 [==============================] - 0s 12ms/step - loss: 0.0142 - val_loss: 0.0125\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<keras.callbacks.History at 0x25e3a8cf640>"
+       "<keras.callbacks.History at 0x1da5016dcd0>"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -551,10 +502,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 3ms/step - loss: 0.0125\n",
+      "3/3 [==============================] - 1s 4ms/step\n",
+      "Loss:  0.012460779398679733\n"
+     ]
+    }
+   ],
+   "source": [
+    "loss            = model.evaluate(X_test, y_test)\n",
+    "test_predict1   = model.predict(X_test)\n",
+    "print(\"Loss: \", loss)\n",
+    "# Converting values back to the original scale\n",
+    "scalerBack      = MinMaxScaler(feature_range=(mintest, maxtest))\n",
+    "test_predict2   = scalerBack.fit_transform(test_predict1)\n",
+    "y_test1         = scalerBack.fit_transform(y_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "%matplotlib qt\n",
+    "\n",
+    "# Create a 3x3 grid of subplots\n",
+    "fig, axes = plt.subplots(3, 3, figsize=(10, 10))\n",
+    "\n",
+    "var = 1\n",
+    "# Loop over the value index\n",
+    "for i, ax in enumerate(axes.flat):\n",
+    "    # Plot your data or perform any other operations\n",
+    "    ax.plot(y_test1[var+i,0:time_step], label='Original Testing Data', color='blue')\n",
+    "    ax.plot(test_predict2[var+i,0:time_step], label='Predicted Testing Data', color='red',alpha=0.8)\n",
+    "    # ax.set_title(f'Plot {i+1}')\n",
+    "    ax.set_title('Testing Data - Predicted vs Actual')\n",
+    "    ax.set_xlabel('Time [hours]')\n",
+    "    ax.set_ylabel('Energy Consumption [kW]')     \n",
+    "    ax.legend()\n",
+    "\n",
+    "# Adjust the spacing between subplots\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "code",