cleaned up notebooks

EnergyLSTM/EDA_lstm_energy.ipynb

@@ -2,13 +2,14 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "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",
@@ -31,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": null,
    "metadata": {
     "vscode": {
      "languageId": "ruby"
@@ -101,26 +102,6 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1d98c8cdfd0>]"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plt.plot(eed_1h['hvac_N'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -140,7 +121,7 @@
     "    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",
+    "    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",
@@ -161,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -195,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -209,7 +190,7 @@
     "    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",
+    "        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",
@@ -225,148 +206,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "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",
-      "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"
-    }
-   ],
+   "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, ii)\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",
-    "# 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 = 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()"
@@ -384,26 +236,6 @@
     "\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 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\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

EnergyLSTM/lstm_energy.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
@@ -92,7 +92,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>23</th>\n",
+       "      <th>0</th>\n",
        "      <td>2018-01-02 00:00:00</td>\n",
        "      <td>38.225000</td>\n",
        "      <td>26.4000</td>\n",
@@ -101,7 +101,7 @@
        "      <td>87.4450</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>24</th>\n",
+       "      <th>1</th>\n",
        "      <td>2018-01-02 01:00:00</td>\n",
        "      <td>38.297501</td>\n",
        "      <td>21.1750</td>\n",
@@ -110,7 +110,7 @@
        "      <td>2.8675</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>25</th>\n",
+       "      <th>2</th>\n",
        "      <td>2018-01-02 02:00:00</td>\n",
        "      <td>38.072500</td>\n",
        "      <td>21.7225</td>\n",
@@ -119,7 +119,7 @@
        "      <td>0.0925</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26</th>\n",
+       "      <th>3</th>\n",
        "      <td>2018-01-02 03:00:00</td>\n",
        "      <td>39.147500</td>\n",
        "      <td>21.7000</td>\n",
@@ -128,7 +128,7 @@
        "      <td>0.1175</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>27</th>\n",
+       "      <th>4</th>\n",
        "      <td>2018-01-02 04:00:00</td>\n",
        "      <td>38.172500</td>\n",
        "      <td>21.6250</td>\n",
@@ -141,22 +141,22 @@
        "</div>"
       ],
       "text/plain": [
-       "                  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",
+       "                 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",
-       "23                87.4450  \n",
-       "24                 2.8675  \n",
-       "25                 0.0925  \n",
-       "26                 0.1175  \n",
-       "27                 0.0725  "
+       "   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": 62,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -184,7 +184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
     {
@@ -193,7 +193,7 @@
        "[]"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -213,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -230,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -255,13 +255,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 94,
    "metadata": {},
-   "outputs": [],
+   "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   = 3   \n",
-    "time_step       = 4*6\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",
@@ -291,9 +328,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 95,
    "metadata": {},
-   "outputs": [],
+   "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",
@@ -306,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -315,12 +362,12 @@
     "# Create a 3x3 grid of subplots\n",
     "fig, axes = plt.subplots(3, 3, figsize=(10, 10))\n",
     "\n",
-    "var = 100\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_test[var+i,0:time_step], label='Original Testing Data', color='blue')\n",
-    "    ax.plot(test_predict1[var+i,0:time_step], label='Predicted Testing Data', color='red',alpha=0.8)\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",
@@ -369,7 +416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [
     {
@@ -377,94 +424,94 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/20\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",
+      "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",
-      "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",
+      "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",
-      "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",
+      "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",
-      "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",
+      "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",
-      "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",
+      "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",
-      "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",
+      "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",
-      "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",
+      "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: 0.0172\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",
+      "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",
-      "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",
+      "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",
-      "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",
+      "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 0.01210\n",
-      "16/16 [==============================] - 0s 13ms/step - loss: 0.0164 - val_loss: 0.0141\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",
-      "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",
+      "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",
-      "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",
+      "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",
-      "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",
+      "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",
-      "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",
+      "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",
-      "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",
       "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 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: 0.0142\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",
+      "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",
-      "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",
+      "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",
-      "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"
+      "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 0x1da5016dcd0>"
+       "<keras.callbacks.History at 0x1da50f44760>"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -494,30 +541,30 @@
     "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",
+    "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",
-    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=20, batch_size=64, verbose=1, callbacks=[checkpoint_callback])"
+    "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": 80,
+   "execution_count": 106,
    "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"
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0109\n",
+      "3/3 [==============================] - 1s 5ms/step\n",
+      "Loss:  0.010930849239230156\n"
      ]
     }
    ],
    "source": [
-    "loss            = model.evaluate(X_test, y_test)\n",
-    "test_predict1   = model.predict(X_test)\n",
+    "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",
@@ -527,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -556,10 +603,15 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model 3 predicting based on past Mondays"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": []
   }
  ],