added training on only similar days

EnergyLSTM/lstm_energy.ipynb

@@ -610,8 +610,212 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "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": []
   }
  ],