diff --git "a/EnergyLSTM/lstm_energy_2.ipynb" "b/EnergyLSTM/lstm_energy_2.ipynb"
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+++ "b/EnergyLSTM/lstm_energy_2.ipynb"
@@ -0,0 +1,759 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from tensorflow import keras\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "energy_df = pd.read_csv(\"../data/extended_energy_data.csv\")\n",
+    "energy_df.bfill(inplace=True)\n",
+    "energy_df.set_index(\"date\", inplace=True)\n",
+    "energy_df.index = pd.to_datetime(energy_df.index)\n",
+    "energy_df = energy_df.resample(\"h\").sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>hvac_N</th>\n",
+       "      <th>hvac_S</th>\n",
+       "      <th>air_temp_set_1</th>\n",
+       "      <th>air_temp_set_2</th>\n",
+       "      <th>dew_point_temperature_set_1d</th>\n",
+       "      <th>relative_humidity_set_1</th>\n",
+       "      <th>solar_radiation_set_1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 00:00:00</th>\n",
+       "      <td>149.600006</td>\n",
+       "      <td>78.000000</td>\n",
+       "      <td>46.160</td>\n",
+       "      <td>45.850</td>\n",
+       "      <td>32.56</td>\n",
+       "      <td>319.29</td>\n",
+       "      <td>205.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 01:00:00</th>\n",
+       "      <td>150.100002</td>\n",
+       "      <td>77.579998</td>\n",
+       "      <td>43.560</td>\n",
+       "      <td>43.730</td>\n",
+       "      <td>31.98</td>\n",
+       "      <td>330.00</td>\n",
+       "      <td>8.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 02:00:00</th>\n",
+       "      <td>151.000004</td>\n",
+       "      <td>91.100000</td>\n",
+       "      <td>43.020</td>\n",
+       "      <td>43.200</td>\n",
+       "      <td>31.84</td>\n",
+       "      <td>332.10</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 03:00:00</th>\n",
+       "      <td>150.200005</td>\n",
+       "      <td>75.680000</td>\n",
+       "      <td>41.910</td>\n",
+       "      <td>42.190</td>\n",
+       "      <td>32.14</td>\n",
+       "      <td>340.00</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 04:00:00</th>\n",
+       "      <td>146.700005</td>\n",
+       "      <td>86.400000</td>\n",
+       "      <td>39.970</td>\n",
+       "      <td>40.590</td>\n",
+       "      <td>32.03</td>\n",
+       "      <td>350.40</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-12-31 20:00:00</th>\n",
+       "      <td>98.483971</td>\n",
+       "      <td>83.099649</td>\n",
+       "      <td>51.078</td>\n",
+       "      <td>44.684</td>\n",
+       "      <td>16.20</td>\n",
+       "      <td>222.79</td>\n",
+       "      <td>1821.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-12-31 21:00:00</th>\n",
+       "      <td>100.596190</td>\n",
+       "      <td>80.819533</td>\n",
+       "      <td>56.156</td>\n",
+       "      <td>49.477</td>\n",
+       "      <td>16.69</td>\n",
+       "      <td>206.86</td>\n",
+       "      <td>1736.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-12-31 22:00:00</th>\n",
+       "      <td>105.769298</td>\n",
+       "      <td>88.237961</td>\n",
+       "      <td>60.655</td>\n",
+       "      <td>53.056</td>\n",
+       "      <td>15.31</td>\n",
+       "      <td>187.71</td>\n",
+       "      <td>1445.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-12-31 23:00:00</th>\n",
+       "      <td>110.242936</td>\n",
+       "      <td>88.951773</td>\n",
+       "      <td>58.988</td>\n",
+       "      <td>53.917</td>\n",
+       "      <td>16.99</td>\n",
+       "      <td>198.63</td>\n",
+       "      <td>910.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-01-01 00:00:00</th>\n",
+       "      <td>29.843889</td>\n",
+       "      <td>23.788947</td>\n",
+       "      <td>13.856</td>\n",
+       "      <td>13.383</td>\n",
+       "      <td>4.64</td>\n",
+       "      <td>54.03</td>\n",
+       "      <td>124.70</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>26305 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         hvac_N     hvac_S  air_temp_set_1  air_temp_set_2  \\\n",
+       "date                                                                         \n",
+       "2018-01-01 00:00:00  149.600006  78.000000          46.160          45.850   \n",
+       "2018-01-01 01:00:00  150.100002  77.579998          43.560          43.730   \n",
+       "2018-01-01 02:00:00  151.000004  91.100000          43.020          43.200   \n",
+       "2018-01-01 03:00:00  150.200005  75.680000          41.910          42.190   \n",
+       "2018-01-01 04:00:00  146.700005  86.400000          39.970          40.590   \n",
+       "...                         ...        ...             ...             ...   \n",
+       "2020-12-31 20:00:00   98.483971  83.099649          51.078          44.684   \n",
+       "2020-12-31 21:00:00  100.596190  80.819533          56.156          49.477   \n",
+       "2020-12-31 22:00:00  105.769298  88.237961          60.655          53.056   \n",
+       "2020-12-31 23:00:00  110.242936  88.951773          58.988          53.917   \n",
+       "2021-01-01 00:00:00   29.843889  23.788947          13.856          13.383   \n",
+       "\n",
+       "                     dew_point_temperature_set_1d  relative_humidity_set_1  \\\n",
+       "date                                                                         \n",
+       "2018-01-01 00:00:00                         32.56                   319.29   \n",
+       "2018-01-01 01:00:00                         31.98                   330.00   \n",
+       "2018-01-01 02:00:00                         31.84                   332.10   \n",
+       "2018-01-01 03:00:00                         32.14                   340.00   \n",
+       "2018-01-01 04:00:00                         32.03                   350.40   \n",
+       "...                                           ...                      ...   \n",
+       "2020-12-31 20:00:00                         16.20                   222.79   \n",
+       "2020-12-31 21:00:00                         16.69                   206.86   \n",
+       "2020-12-31 22:00:00                         15.31                   187.71   \n",
+       "2020-12-31 23:00:00                         16.99                   198.63   \n",
+       "2021-01-01 00:00:00                          4.64                    54.03   \n",
+       "\n",
+       "                     solar_radiation_set_1  \n",
+       "date                                        \n",
+       "2018-01-01 00:00:00                 205.63  \n",
+       "2018-01-01 01:00:00                   8.50  \n",
+       "2018-01-01 02:00:00                   0.00  \n",
+       "2018-01-01 03:00:00                   0.00  \n",
+       "2018-01-01 04:00:00                   0.00  \n",
+       "...                                    ...  \n",
+       "2020-12-31 20:00:00                1821.80  \n",
+       "2020-12-31 21:00:00                1736.20  \n",
+       "2020-12-31 22:00:00                1445.60  \n",
+       "2020-12-31 23:00:00                 910.80  \n",
+       "2021-01-01 00:00:00                 124.70  \n",
+       "\n",
+       "[26305 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "energy_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "energy_data = energy_df.loc[:, ['hvac_N']].ewm(com =2).mean()\n",
+    "\n",
+    "#encode index sinusoidal for day of the week\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "\n",
+    "energy_data['day_of_week'] = energy_data.index.dayofweek\n",
+    "energy_data['hour_of_day'] = energy_data.index.hour\n",
+    "energy_data['scaled_hvac_N'] = scaler.fit_transform(energy_data[['hvac_N']])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "energy_data['day_encoding'] = np.sin(2*np.pi*energy_data['day_of_week']/7)\n",
+    "energy_data['hour_encoding'] = np.sin(2*np.pi*energy_data['hour_of_day']/24)\n",
+    "energy_data['month_encoding'] = np.sin(2*np.pi*energy_data.index.month/12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x215bab56bd0>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(energy_data['scaled_hvac_N'][100+0:100+168])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = energy_data.loc[:,['scaled_hvac_N', 'day_encoding', 'hour_encoding', 'month_encoding']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data, test_data = train_test_split(data, test_size=0.2, shuffle=False)\n",
+    "train_data, val_data = train_test_split(train_data, test_size=0.2, shuffle=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create dataset for lstm with window_size\n",
+    "\n",
+    "window_size = 7*24\n",
+    "\n",
+    "def create_dataset(data, window_size):\n",
+    "    data = data.values\n",
+    "    X, y = [], []\n",
+    "    for i in range(len(data) - window_size):\n",
+    "        X.append(data[i:i+window_size])\n",
+    "        y.append(data[i+window_size][0])\n",
+    "    return np.array(X), np.array(y)\n",
+    "\n",
+    "X_train, y_train = create_dataset(train_data, window_size)\n",
+    "X_val, y_val = create_dataset(val_data, window_size)\n",
+    "X_test, y_test = create_dataset(test_data, window_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# shuffle X_train and y_train  \n",
+    "shuffle_idx = np.random.permutation(len(X_train))\n",
+    "X_train = X_train[shuffle_idx]\n",
+    "y_train = y_train[shuffle_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(16667, 168, 4)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = keras.models.Sequential()\n",
+    "model.add(keras.Input(shape = (X_train.shape[1], X_train.shape[2])))\n",
+    "# model.add(keras.layers.LSTM(50,  return_sequences = True))\n",
+    "model.add(keras.layers.LSTM(units=50, return_sequences=True))\n",
+    "model.add(keras.layers.LSTM(units=50))\n",
+    "model.add(keras.layers.Dense(1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">168</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">11,000</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m168\u001b[0m, \u001b[38;5;34m50\u001b[0m)        │        \u001b[38;5;34m11,000\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">31,251</span> (122.07 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m31,251\u001b[0m (122.07 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">31,251</span> (122.07 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m31,251\u001b[0m (122.07 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "\u001b[1m521/521\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 119ms/step - loss: 0.1708 - val_loss: 0.0219\n",
+      "Epoch 2/10\n",
+      "\u001b[1m521/521\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 153ms/step - loss: 0.0143 - val_loss: 0.0078\n",
+      "Epoch 3/10\n",
+      "\u001b[1m521/521\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 154ms/step - loss: 0.0113 - val_loss: 0.0068\n",
+      "Epoch 4/10\n",
+      "\u001b[1m521/521\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 153ms/step - loss: 0.0113 - val_loss: 0.0070\n",
+      "Epoch 5/10\n",
+      "\u001b[1m521/521\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 152ms/step - loss: 0.0108 - val_loss: 0.0097\n",
+      "Epoch 6/10\n",
+      "\u001b[1m521/521\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 155ms/step - loss: 0.0116 - val_loss: 0.0067\n",
+      "Epoch 7/10\n",
+      "\u001b[1m521/521\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 151ms/step - loss: 0.0107 - val_loss: 0.0071\n",
+      "Epoch 8/10\n",
+      "\u001b[1m367/521\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m17s\u001b[0m 115ms/step - loss: 0.0112"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[14], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m model\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madam\u001b[39m\u001b[38;5;124m'\u001b[39m, loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmean_squared_error\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_val\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_val\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:314\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[0;32m    312\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator\u001b[38;5;241m.\u001b[39menumerate_epoch():\n\u001b[0;32m    313\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[1;32m--> 314\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    315\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pythonify_logs(logs)\n\u001b[0;32m    316\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(step, logs)\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:833\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    830\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    832\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 833\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    835\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m    836\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:878\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    875\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[0;32m    876\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[0;32m    877\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[1;32m--> 878\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mtracing_compilation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable_creation_config\u001b[49m\n\u001b[0;32m    880\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    881\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[0;32m    882\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    883\u001b[0m                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[1;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[0;32m    137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[1;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[0;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflat_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\n\u001b[0;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\concrete_function.py:1322\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[0;32m   1318\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m   1319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m   1320\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m   1321\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1322\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_preflattened\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1323\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m   1324\u001b[0m     args,\n\u001b[0;32m   1325\u001b[0m     possible_gradient_type,\n\u001b[0;32m   1326\u001b[0m     executing_eagerly)\n\u001b[0;32m   1327\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[1;34m(self, args)\u001b[0m\n\u001b[0;32m    214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m    215\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 216\u001b[0m   flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    217\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[0;32m    250\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[1;32m--> 251\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    256\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    257\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[0;32m    258\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    259\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[0;32m    260\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[0;32m    261\u001b[0m     )\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\context.py:1500\u001b[0m, in \u001b[0;36mContext.call_function\u001b[1;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[0;32m   1498\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[0;32m   1499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1500\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1501\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1502\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1503\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1504\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1505\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1506\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1507\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1508\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m   1509\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m   1510\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1514\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[0;32m   1515\u001b[0m   )\n",
+      "File \u001b[1;32md:\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     54\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "model.fit(X_train, y_train, validation_data = (X_val, y_val), verbose = 1, epochs = 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m160/160\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 43ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "# predict on test\n",
+    "\n",
+    "y_pred = model.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x215d2435610>]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot y_test and y_pred\n",
+    "\n",
+    "plt.figure(figsize=(14, 7))\n",
+    "plt.plot(y_test, label = 'True')\n",
+    "plt.plot(y_pred, label = 'Predicted')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get weekly data from test_data_df \n",
+    "\n",
+    "weekly_window = 7*24\n",
+    "daily_window = 24\n",
+    "\n",
+    "def create_forecast_dataset(data):\n",
+    "    X, y = [], []\n",
+    "\n",
+    "    for i in range(len(data) - weekly_window - daily_window):\n",
+    "        X.append(data[i:i+weekly_window])\n",
+    "        y.append(data[i+weekly_window:i+weekly_window+daily_window])\n",
+    "\n",
+    "    return np.array(X), np.array(y)\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, y = create_forecast_dataset(train_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(16643, 168, 4)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(16643, 24, 4)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.43388374,  1.        ,  0.5       ])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y[100][2][1:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_data = X[200]\n",
+    "y_forecast = []\n",
+    "for i in range(24):\n",
+    "    y_forecast.append(model.predict(input_data.reshape(1, weekly_window, 4), verbose = False).flatten().item())\n",
+    "    new_time_encodings = y[200][i][1:]\n",
+    "    new_input = np.concatenate([np.array(y_forecast[-1]).reshape(1,1), new_time_encodings.reshape(1,3)], axis=1)\n",
+    "    input_data = np.concatenate([input_data[1:], new_input], axis=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x215d809a810>]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(y[200,:,0])\n",
+    "plt.plot(y_forecast)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
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