Merge branch 'lstm' of hf.co:spaces/smartbuildings/smart-buildings into lstm

.gitignore

@@ -3,4 +3,5 @@ venv
 .vscode
 __pycache__/
 *.tf
-data
+data
+*.keras

EnergyLSTM/EDA_lstm_energy.ipynb

@@ -0,0 +1,262 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "from datetime import datetime \n",
+    "from datetime import timedelta\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 statsmodels.tsa.holtwinters import ExponentialSmoothing\n",
+    "\n",
+    "dataPATH = r\"C:\\Users\\levim\\OneDrive\\Documents\\MastersAI_ES\\TeamProject-5ARIP10\\smart-buildings\\Data\"\n",
+    "\n",
+    "### Load ALL data ###\n",
+    "# all_data = pd.read_csv(dataPATH + r\"\\long_merge.csv\")\n",
+    "all_data = pd.read_csv(dataPATH + r\"\\extended_energy_data.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load selection of data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "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 = extended energy data\n",
+    "# Resampling back to 15 minutes and 1 hour\n",
+    "eed_15m = extended_energy_data.resample('15T').mean()\n",
+    "eed_1h = extended_energy_data.resample('60T').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "ruby"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Assuming you want to apply a moving average window of size 3 on the 'column_name' column\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()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib qt\n",
+    "\n",
+    "start_date  = '2018-06-02'\n",
+    "end_date    = '2018-06-08'\n",
+    "\n",
+    "plt.plot(eed_15m['hvac_N'].loc[start_date:end_date])\n",
+    "plt.plot(eed_15m_avg['hvac_N'].loc[start_date:end_date])\n",
+    "plt.plot(eed_1h_avg['hvac_N'].loc[start_date:end_date])\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib qt\n",
+    "\n",
+    "plt.figure(figsize=(20,10))\n",
+    "plt.plot(eed_1h['hvac_S'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Filling data gaps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fillgap(firstTS, secondTS, seasonal_periods):\n",
+    "    \n",
+    "    #PREPARATION\n",
+    "    one = timedelta(hours=1)\n",
+    "    secondTSr = secondTS[::-1].copy()\n",
+    "    firstTSr = firstTS[::-1].copy()\n",
+    "    indexr = pd.date_range(start=firstTS.index[0], end=secondTS.index[-1], freq='H')\n",
+    "    firstTSr.index = indexr[-len(firstTSr):]\n",
+    "    secondTSr.index = indexr[:len(secondTSr)]\n",
+    "    \n",
+    "    #FORWARD    \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', freq='H').fit()\n",
+    "    backwardPrediction = es.predict(start=secondTSr.index[-1]+one, end=firstTSr.index[0]-one)\n",
+    "    \n",
+    "    #INTERPOLATION\n",
+    "    l = len(forwardPrediction)\n",
+    "    interpolation = pd.Series([(backwardPrediction[i] * i + forwardPrediction[i] * (l -i) )/ l for i in range(l)], index=forwardPrediction.index.copy())\n",
+    "  \n",
+    "    return interpolation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to split the data into multiple DataFrames based on the gaps\n",
+    "def split_dfs(data):\n",
+    "\n",
+    "    # Prepare the DataFrame\n",
+    "    df = data.copy()\n",
+    "    df = df.reset_index()\n",
+    "    df = df.dropna()\n",
+    "    \n",
+    "    # Set the maximum allowable gap (e.g., 1 hour)\n",
+    "    max_gap = pd.Timedelta(hours=1)\n",
+    "\n",
+    "    # Calculate the differences between consecutive timestamps\n",
+    "    time_diff = df['date'].diff()\n",
+    "\n",
+    "    # Identify gaps larger than the maximum allowable gap\n",
+    "    gaps = time_diff > max_gap\n",
+    "\n",
+    "    # Create a new column to identify different groups\n",
+    "    df['group'] = gaps.cumsum()\n",
+    "\n",
+    "    df.set_index('date', inplace=True)\n",
+    "\n",
+    "    # Split the DataFrame into a list of DataFrames based on the groups\n",
+    "    dfs = [group for _, group in df.groupby('group')]\n",
+    "\n",
+    "    return dfs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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 > 24*7: # 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",
+    "        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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# interpolation of the whole data set\n",
+    "\n",
+    "ip_eed_1h = pd.DataFrame()\n",
+    "for ii in eed_1h.columns:\n",
+    "    ip_df = interpolate_gaps(eed_1h['2018-1-2':], 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",
+    "ip_eed_1h = ip_eed_1h.set_axis('date', axis=0)\n",
+    "ip_eed_1h.to_csv(dataPATH + r\"\\interpolated_energy_data.csv\")\n",
+    "\n",
+    "ip_eed_1h.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib qt\n",
+    "# plt.plot(eed_1h['hvac_N'])\n",
+    "plt.plot(ip_df)\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "experiments",
+   "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": "3.8.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

EnergyLSTM/lstm_energy.ipynb

@@ -0,0 +1,843 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "from datetime import datetime \n",
+    "from datetime import date\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import LSTM, Dense\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import MinMaxScaler,StandardScaler\n",
+    "from keras.callbacks import ModelCheckpoint\n",
+    "\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\")\n",
+    "interpolated_data = pd.read_csv(dataPATH + r\"\\interpolated_energy_data.csv\", index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "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": 86,
+   "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>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>2018-01-02 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>2018-01-02 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>2018-01-02 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>2018-01-02 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>2018-01-02 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  day_of_week  air_temp_set_1  \\\n",
+       "0 2018-01-02 00:00:00  38.225000  26.4000            1         14.9550   \n",
+       "1 2018-01-02 01:00:00  38.297501  21.1750            1         14.2125   \n",
+       "2 2018-01-02 02:00:00  38.072500  21.7225            1         14.2700   \n",
+       "3 2018-01-02 03:00:00  39.147500  21.7000            1         14.1375   \n",
+       "4 2018-01-02 04:00:00  38.172500  21.6250            1         13.9850   \n",
+       "\n",
+       "   solar_radiation_set_1  \n",
+       "0                87.4450  \n",
+       "1                 2.8675  \n",
+       "2                 0.0925  \n",
+       "3                 0.1175  \n",
+       "4                 0.0725  "
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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 = 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.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(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.\")\n",
+    "\n",
+    "df_filtered.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "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, 3, 1)) | (df_filtered.date.dt.date > date(2019, 6, 1))]\n",
+    "\n",
+    "testdataset = testdataset_df.drop(columns=[\"date\"]).values\n",
+    "\n",
+    "traindataset = traindataset_df.drop(columns=[\"date\"]).values\n",
+    "\n",
+    "columns_with_na = traindataset_df.columns[traindataset_df.isna().any()].tolist()\n",
+    "columns_with_na"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "traindataset = traindataset.astype('float32')\n",
+    "testdataset = testdataset.astype('float32')\n",
+    "\n",
+    "mintest = np.min(testdataset[:,0:2])\n",
+    "maxtest = np.max(testdataset[:,0:2])\n",
+    "\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "traindataset = scaler.fit_transform(traindataset)\n",
+    "testdataset = scaler.transform(testdataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_model(X_train, time_step, no_outputs):\n",
+    "    model = Sequential()\n",
+    "    model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
+    "    model.add(LSTM(units=50, return_sequences=True))\n",
+    "    model.add(LSTM(units=time_step*no_outputs))\n",
+    "    model.add(Dense(units=time_step*no_outputs))\n",
+    "\n",
+    "    model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model 1 (continous predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "370/371 [============================>.] - ETA: 0s - loss: 0.0224\n",
+      "Epoch 1: val_loss improved from inf to 0.01162, saving model to lstm_energy_01.keras\n",
+      "371/371 [==============================] - 11s 15ms/step - loss: 0.0224 - val_loss: 0.0116\n",
+      "Epoch 2/5\n",
+      "368/371 [============================>.] - ETA: 0s - loss: 0.0139\n",
+      "Epoch 2: val_loss improved from 0.01162 to 0.01146, saving model to lstm_energy_01.keras\n",
+      "371/371 [==============================] - 5s 12ms/step - loss: 0.0139 - val_loss: 0.0115\n",
+      "Epoch 3/5\n",
+      "370/371 [============================>.] - ETA: 0s - loss: 0.0125\n",
+      "Epoch 3: val_loss improved from 0.01146 to 0.01132, saving model to lstm_energy_01.keras\n",
+      "371/371 [==============================] - 5s 13ms/step - loss: 0.0125 - val_loss: 0.0113\n",
+      "Epoch 4/5\n",
+      "367/371 [============================>.] - ETA: 0s - loss: 0.0119\n",
+      "Epoch 4: val_loss improved from 0.01132 to 0.01007, saving model to lstm_energy_01.keras\n",
+      "371/371 [==============================] - 5s 13ms/step - loss: 0.0119 - val_loss: 0.0101\n",
+      "Epoch 5/5\n",
+      "371/371 [==============================] - ETA: 0s - loss: 0.0117\n",
+      "Epoch 5: val_loss did not improve from 0.01007\n",
+      "371/371 [==============================] - 5s 13ms/step - loss: 0.0117 - val_loss: 0.0101\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x1da353bd790>"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train,test = traindataset,testdataset\n",
+    "steps_in_past   = 7   \n",
+    "time_step       = 24\n",
+    "no_inputs       = 5\n",
+    "no_outputs      = 2\n",
+    "def create_dataset(dataset,time_step):\n",
+    "    x = [[] for _ in range(no_inputs)] \n",
+    "    Y = [[] for _ in range(no_outputs)]\n",
+    "    for i in range(time_step * steps_in_past, len(dataset) - time_step * steps_in_past): # -time_step is to ensure that the Y value has enough values\n",
+    "        for j in range(no_inputs):\n",
+    "            x[j].append(dataset[(i-time_step*steps_in_past):i, j])\n",
+    "        for j in range(no_outputs):\n",
+    "            Y[j].append(dataset[i:i+time_step, j])    \n",
+    "    x = [np.array(feature_list) for feature_list in x]\n",
+    "    x = np.stack(x,axis=1)\n",
+    "    Y = [np.array(feature_list) for feature_list in Y] \n",
+    "    Y = np.stack(Y,axis=1)\n",
+    "    Y = np.reshape(Y, (Y.shape[0], time_step*no_outputs))\n",
+    "    return x, Y\n",
+    "\n",
+    "\n",
+    "X_train, y_train = create_dataset(train, time_step)\n",
+    "X_test, y_test = create_dataset(test, time_step)\n",
+    "\n",
+    "model = create_model(X_train, time_step, no_outputs)\n",
+    "checkpoint_path = \"lstm_energy_01.keras\"\n",
+    "checkpoint_callback = ModelCheckpoint(filepath=checkpoint_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
+    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=64, verbose=1, callbacks=[checkpoint_callback])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "60/60 [==============================] - 0s 4ms/step - loss: 0.0101\n",
+      "60/60 [==============================] - 1s 3ms/step\n",
+      "Loss:  0.010141444392502308\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": 100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib qt\n",
+    "\n",
+    "# Create a 3x3 grid of subplots\n",
+    "fig, axes = plt.subplots(3, 3, figsize=(10, 10))\n",
+    "\n",
+    "var = 15\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*9,0:time_step], label='Original Testing Data', color='blue')\n",
+    "    ax.plot(test_predict2[var+i*9,0: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",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Autoregressive prediction\n",
+    "X_pred = testdataset.copy()\n",
+    "for i in range(steps_in_past,steps_in_past*2):\n",
+    "    xin = X_pred[i-steps_in_past:i].reshape((1, steps_in_past, no_outputs)) \n",
+    "    X_pred[i] = model.predict(xin, verbose = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot prediction vs actual for test data\n",
+    "plt.figure()\n",
+    "plt.plot(X_pred[steps_in_past:steps_in_past*2,0],':',label='LSTM')\n",
+    "plt.plot(testdataset[steps_in_past:steps_in_past*2,0],'--',label='Actual')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model 2 (Predicting once per day)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "13/16 [=======================>......] - ETA: 0s - loss: 0.0893\n",
+      "Epoch 1: val_loss improved from inf to 0.02898, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 6s 100ms/step - loss: 0.0820 - val_loss: 0.0290\n",
+      "Epoch 2/20\n",
+      "13/16 [=======================>......] - ETA: 0s - loss: 0.0316\n",
+      "Epoch 2: val_loss improved from 0.02898 to 0.02435, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 20ms/step - loss: 0.0310 - val_loss: 0.0243\n",
+      "Epoch 3/20\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0242\n",
+      "Epoch 3: val_loss improved from 0.02435 to 0.01740, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 24ms/step - loss: 0.0242 - val_loss: 0.0174\n",
+      "Epoch 4/20\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0213\n",
+      "Epoch 4: val_loss improved from 0.01740 to 0.01566, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 25ms/step - loss: 0.0213 - val_loss: 0.0157\n",
+      "Epoch 5/20\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0189\n",
+      "Epoch 5: val_loss improved from 0.01566 to 0.01483, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 25ms/step - loss: 0.0189 - val_loss: 0.0148\n",
+      "Epoch 6/20\n",
+      "13/16 [=======================>......] - ETA: 0s - loss: 0.0184\n",
+      "Epoch 6: val_loss improved from 0.01483 to 0.01359, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 25ms/step - loss: 0.0182 - val_loss: 0.0136\n",
+      "Epoch 7/20\n",
+      "14/16 [=========================>....] - ETA: 0s - loss: 0.0177\n",
+      "Epoch 7: val_loss improved from 0.01359 to 0.01285, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 22ms/step - loss: 0.0175 - val_loss: 0.0128\n",
+      "Epoch 8/20\n",
+      "13/16 [=======================>......] - ETA: 0s - loss: 0.0168\n",
+      "Epoch 8: val_loss did not improve from 0.01285\n",
+      "16/16 [==============================] - 0s 20ms/step - loss: 0.0171 - val_loss: 0.0148\n",
+      "Epoch 9/20\n",
+      "14/16 [=========================>....] - ETA: 0s - loss: 0.0178\n",
+      "Epoch 9: val_loss did not improve from 0.01285\n",
+      "16/16 [==============================] - 0s 20ms/step - loss: 0.0175 - val_loss: 0.0143\n",
+      "Epoch 10/20\n",
+      "15/16 [===========================>..] - ETA: 0s - loss: 0.0165\n",
+      "Epoch 10: val_loss improved from 0.01285 to 0.01277, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 22ms/step - loss: 0.0166 - val_loss: 0.0128\n",
+      "Epoch 11/20\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0164\n",
+      "Epoch 11: val_loss did not improve from 0.01277\n",
+      "16/16 [==============================] - 0s 23ms/step - loss: 0.0164 - val_loss: 0.0139\n",
+      "Epoch 12/20\n",
+      "15/16 [===========================>..] - ETA: 0s - loss: 0.0162\n",
+      "Epoch 12: val_loss improved from 0.01277 to 0.01235, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 1s 33ms/step - loss: 0.0162 - val_loss: 0.0124\n",
+      "Epoch 13/20\n",
+      "15/16 [===========================>..] - ETA: 0s - loss: 0.0154\n",
+      "Epoch 13: val_loss did not improve from 0.01235\n",
+      "16/16 [==============================] - 0s 20ms/step - loss: 0.0153 - val_loss: 0.0131\n",
+      "Epoch 14/20\n",
+      "13/16 [=======================>......] - ETA: 0s - loss: 0.0156\n",
+      "Epoch 14: val_loss did not improve from 0.01235\n",
+      "16/16 [==============================] - 0s 21ms/step - loss: 0.0160 - val_loss: 0.0136\n",
+      "Epoch 15/20\n",
+      "13/16 [=======================>......] - ETA: 0s - loss: 0.0167\n",
+      "Epoch 15: val_loss did not improve from 0.01235\n",
+      "16/16 [==============================] - 0s 20ms/step - loss: 0.0164 - val_loss: 0.0125\n",
+      "Epoch 16/20\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0149\n",
+      "Epoch 16: val_loss improved from 0.01235 to 0.01134, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 25ms/step - loss: 0.0149 - val_loss: 0.0113\n",
+      "Epoch 17/20\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0147\n",
+      "Epoch 17: val_loss did not improve from 0.01134\n",
+      "16/16 [==============================] - 0s 21ms/step - loss: 0.0147 - val_loss: 0.0125\n",
+      "Epoch 18/20\n",
+      "15/16 [===========================>..] - ETA: 0s - loss: 0.0143\n",
+      "Epoch 18: val_loss did not improve from 0.01134\n",
+      "16/16 [==============================] - 0s 23ms/step - loss: 0.0143 - val_loss: 0.0116\n",
+      "Epoch 19/20\n",
+      "15/16 [===========================>..] - ETA: 0s - loss: 0.0138\n",
+      "Epoch 19: val_loss improved from 0.01134 to 0.01108, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 23ms/step - loss: 0.0138 - val_loss: 0.0111\n",
+      "Epoch 20/20\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0137\n",
+      "Epoch 20: val_loss improved from 0.01108 to 0.01093, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 25ms/step - loss: 0.0137 - val_loss: 0.0109\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x1da50f44760>"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train,test = traindataset,testdataset\n",
+    "steps_in_past   = 7   \n",
+    "time_step       = 24\n",
+    "no_inputs       = 5\n",
+    "no_outputs      = 2\n",
+    "def create_dataset(dataset,time_step):\n",
+    "    x = [[] for _ in range(no_inputs)] \n",
+    "    Y = [[] for _ in range(no_outputs)]\n",
+    "    for i in range(steps_in_past, round(len(dataset)/24) - steps_in_past): # -time_step is to ensure that the Y value has enough values\n",
+    "        for j in range(no_inputs):\n",
+    "            x[j].append(dataset[(i-steps_in_past)*time_step:i*time_step, j])\n",
+    "        for j in range(no_outputs):\n",
+    "            Y[j].append(dataset[i*time_step:(i+1)*time_step, j])    \n",
+    "    x = [np.array(feature_list) for feature_list in x]\n",
+    "    x = np.stack(x,axis=1)\n",
+    "    Y = [np.array(feature_list) for feature_list in Y] \n",
+    "    Y = np.stack(Y,axis=1)\n",
+    "    Y = np.reshape(Y, (Y.shape[0], time_step*no_outputs))\n",
+    "    return x, Y\n",
+    "\n",
+    "\n",
+    "X_train, y_train = create_dataset(train, time_step)\n",
+    "X_test, y_test = create_dataset(test, time_step)\n",
+    "\n",
+    "model2 = create_model(X_train, time_step, no_outputs)\n",
+    "checkpoint_path = \"lstm_energy_01.keras\"\n",
+    "checkpoint_callback = ModelCheckpoint(filepath=checkpoint_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
+    "model2.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=20, batch_size=64, verbose=1, callbacks=[checkpoint_callback])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0109\n",
+      "3/3 [==============================] - 1s 5ms/step\n",
+      "Loss:  0.010930849239230156\n"
+     ]
+    }
+   ],
+   "source": [
+    "loss            = model2.evaluate(X_test, y_test)\n",
+    "test_predict1   = model2.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": 107,
+   "metadata": {},
+   "outputs": [],
+   "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": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model 3 predicting based on past Mondays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0888\n",
+      "Epoch 1: val_loss improved from inf to 0.02289, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 7s 109ms/step - loss: 0.0888 - val_loss: 0.0229\n",
+      "Epoch 2/20\n",
+      "13/16 [=======================>......] - ETA: 0s - loss: 0.0288\n",
+      "Epoch 2: val_loss improved from 0.02289 to 0.01442, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 25ms/step - loss: 0.0276 - val_loss: 0.0144\n",
+      "Epoch 3/20\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0197\n",
+      "Epoch 3: val_loss improved from 0.01442 to 0.01279, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 25ms/step - loss: 0.0197 - val_loss: 0.0128\n",
+      "Epoch 4/20\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0186\n",
+      "Epoch 4: val_loss improved from 0.01279 to 0.01133, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 26ms/step - loss: 0.0186 - val_loss: 0.0113\n",
+      "Epoch 5/20\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0183\n",
+      "Epoch 5: val_loss improved from 0.01133 to 0.01111, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 22ms/step - loss: 0.0183 - val_loss: 0.0111\n",
+      "Epoch 6/20\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0183\n",
+      "Epoch 6: val_loss did not improve from 0.01111\n",
+      "16/16 [==============================] - 0s 24ms/step - loss: 0.0183 - val_loss: 0.0113\n",
+      "Epoch 7/20\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0177\n",
+      "Epoch 7: val_loss did not improve from 0.01111\n",
+      "16/16 [==============================] - 0s 23ms/step - loss: 0.0177 - val_loss: 0.0112\n",
+      "Epoch 8/20\n",
+      "15/16 [===========================>..] - ETA: 0s - loss: 0.0176\n",
+      "Epoch 8: val_loss improved from 0.01111 to 0.01089, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 22ms/step - loss: 0.0177 - val_loss: 0.0109\n",
+      "Epoch 9/20\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0170\n",
+      "Epoch 9: val_loss improved from 0.01089 to 0.01028, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 27ms/step - loss: 0.0170 - val_loss: 0.0103\n",
+      "Epoch 10/20\n",
+      "13/16 [=======================>......] - ETA: 0s - loss: 0.0164\n",
+      "Epoch 10: val_loss improved from 0.01028 to 0.00991, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 23ms/step - loss: 0.0164 - val_loss: 0.0099\n",
+      "Epoch 11/20\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0162\n",
+      "Epoch 11: val_loss improved from 0.00991 to 0.00951, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 25ms/step - loss: 0.0162 - val_loss: 0.0095\n",
+      "Epoch 12/20\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0156\n",
+      "Epoch 12: val_loss improved from 0.00951 to 0.00937, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 27ms/step - loss: 0.0156 - val_loss: 0.0094\n",
+      "Epoch 13/20\n",
+      "13/16 [=======================>......] - ETA: 0s - loss: 0.0151\n",
+      "Epoch 13: val_loss improved from 0.00937 to 0.00884, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 22ms/step - loss: 0.0151 - val_loss: 0.0088\n",
+      "Epoch 14/20\n",
+      "15/16 [===========================>..] - ETA: 0s - loss: 0.0151\n",
+      "Epoch 14: val_loss improved from 0.00884 to 0.00858, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 27ms/step - loss: 0.0150 - val_loss: 0.0086\n",
+      "Epoch 15/20\n",
+      "13/16 [=======================>......] - ETA: 0s - loss: 0.0140\n",
+      "Epoch 15: val_loss improved from 0.00858 to 0.00820, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 24ms/step - loss: 0.0141 - val_loss: 0.0082\n",
+      "Epoch 16/20\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0138\n",
+      "Epoch 16: val_loss did not improve from 0.00820\n",
+      "16/16 [==============================] - 0s 22ms/step - loss: 0.0138 - val_loss: 0.0083\n",
+      "Epoch 17/20\n",
+      "15/16 [===========================>..] - ETA: 0s - loss: 0.0134\n",
+      "Epoch 17: val_loss improved from 0.00820 to 0.00776, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 1s 34ms/step - loss: 0.0133 - val_loss: 0.0078\n",
+      "Epoch 18/20\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0128\n",
+      "Epoch 18: val_loss improved from 0.00776 to 0.00728, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 27ms/step - loss: 0.0128 - val_loss: 0.0073\n",
+      "Epoch 19/20\n",
+      "16/16 [==============================] - ETA: 0s - loss: 0.0119\n",
+      "Epoch 19: val_loss improved from 0.00728 to 0.00668, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 27ms/step - loss: 0.0119 - val_loss: 0.0067\n",
+      "Epoch 20/20\n",
+      "13/16 [=======================>......] - ETA: 0s - loss: 0.0118\n",
+      "Epoch 20: val_loss improved from 0.00668 to 0.00635, saving model to lstm_energy_01.keras\n",
+      "16/16 [==============================] - 0s 23ms/step - loss: 0.0118 - val_loss: 0.0064\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x1da6976bcd0>"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train,test = traindataset,testdataset\n",
+    "days_in_past    = 3     # number of days to look back  \n",
+    "time_step       = 24    # define a day in hours\n",
+    "no_inputs       = 2\n",
+    "no_outputs      = 2\n",
+    "def create_dataset(dataset,time_step):\n",
+    "    x = [[] for _ in range(no_inputs*days_in_past)] \n",
+    "    Y = [[] for _ in range(no_outputs)]\n",
+    "    for i in range(days_in_past*7, round(len(dataset)/time_step) - days_in_past): # -time_step is to ensure that the Y value has enough values\n",
+    "        for k in range(no_inputs*days_in_past):\n",
+    "                if k > 3:\n",
+    "                     j = 1\n",
+    "                     l = k - 4\n",
+    "                     x[k].append(dataset[(i-l*7)*time_step:(i-l*7+1)*time_step, j])\n",
+    "                else:\n",
+    "                     j = 0\n",
+    "                     x[k].append(dataset[(i-k*7)*time_step:(i-k*7+1)*time_step, j])\n",
+    "                \n",
+    "        for j in range(no_outputs):\n",
+    "            Y[j].append(dataset[i*time_step:(i+1)*time_step, j])    \n",
+    "    x = [np.array(feature_list) for feature_list in x]\n",
+    "    x = np.stack(x,axis=1)\n",
+    "    Y = [np.array(feature_list) for feature_list in Y] \n",
+    "    Y = np.stack(Y,axis=1)\n",
+    "    Y = np.reshape(Y, (Y.shape[0], time_step*no_outputs))\n",
+    "    return x, Y\n",
+    "\n",
+    "\n",
+    "X_train, y_train = create_dataset(train, time_step)\n",
+    "X_test, y_test = create_dataset(test, time_step)\n",
+    "\n",
+    "model3 = create_model(X_train, time_step, no_outputs)\n",
+    "checkpoint_path = \"lstm_energy_01.keras\"\n",
+    "checkpoint_callback = ModelCheckpoint(filepath=checkpoint_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
+    "model3.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=20, batch_size=64, verbose=1, callbacks=[checkpoint_callback])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0064\n",
+      "3/3 [==============================] - 1s 4ms/step\n",
+      "Loss:  0.00635459553450346\n"
+     ]
+    }
+   ],
+   "source": [
+    "loss            = model3.evaluate(X_test, y_test)\n",
+    "test_predict1   = model3.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": 143,
+   "metadata": {},
+   "outputs": [],
+   "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",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "experiments",
+   "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": "3.8.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}