diff --git "a/lstm.ipynb" "b/lstm.ipynb"
--- "a/lstm.ipynb"
+++ "b/lstm.ipynb"
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -463,7 +463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -502,7 +502,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -551,9 +551,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n"
+     ]
+    },
     {
      "name": "stderr",
      "output_type": "stream",
@@ -566,57 +573,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/10\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0071\n",
-      "Epoch 1: val_loss improved from inf to 0.01145, saving model to lstm2.keras\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 23ms/step - loss: 0.0071 - val_loss: 0.0115\n",
-      "Epoch 2/10\n",
-      "\u001b[1m3217/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0013\n",
-      "Epoch 2: val_loss improved from 0.01145 to 0.01144, saving model to lstm2.keras\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 23ms/step - loss: 0.0013 - val_loss: 0.0114\n",
-      "Epoch 3/10\n",
-      "\u001b[1m3218/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0010\n",
-      "Epoch 3: val_loss improved from 0.01144 to 0.00729, saving model to lstm2.keras\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 22ms/step - loss: 0.0010 - val_loss: 0.0073\n",
-      "Epoch 4/10\n",
-      "\u001b[1m3218/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 5.5876e-04\n",
-      "Epoch 4: val_loss improved from 0.00729 to 0.00409, saving model to lstm2.keras\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 23ms/step - loss: 5.5871e-04 - val_loss: 0.0041\n",
-      "Epoch 5/10\n",
-      "\u001b[1m3218/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 3.9261e-04\n",
-      "Epoch 5: val_loss improved from 0.00409 to 0.00386, saving model to lstm2.keras\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 22ms/step - loss: 3.9260e-04 - val_loss: 0.0039\n",
-      "Epoch 6/10\n",
-      "\u001b[1m3219/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 3.3977e-04\n",
-      "Epoch 6: val_loss did not improve from 0.00386\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 22ms/step - loss: 3.3976e-04 - val_loss: 0.0049\n",
-      "Epoch 7/10\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 3.0365e-04\n",
-      "Epoch 7: val_loss did not improve from 0.00386\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m69s\u001b[0m 22ms/step - loss: 3.0364e-04 - val_loss: 0.0052\n",
-      "Epoch 8/10\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.7422e-04\n",
-      "Epoch 8: val_loss did not improve from 0.00386\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 22ms/step - loss: 2.7422e-04 - val_loss: 0.0052\n",
-      "Epoch 9/10\n",
-      "\u001b[1m3217/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.5380e-04\n",
-      "Epoch 9: val_loss did not improve from 0.00386\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 23ms/step - loss: 2.5379e-04 - val_loss: 0.0058\n",
-      "Epoch 10/10\n",
-      "\u001b[1m3219/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.3404e-04\n",
-      "Epoch 10: val_loss did not improve from 0.00386\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 22ms/step - loss: 2.3403e-04 - val_loss: 0.0099\n"
+      "\u001b[1m2745/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m6s\u001b[0m 14ms/step - loss: 0.0100"
      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "<keras.src.callbacks.history.History at 0x2a05f762150>"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
+     "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[6], line 38\u001b[0m\n\u001b[0;32m     36\u001b[0m checkpoint_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlstm2.keras\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     37\u001b[0m checkpoint_callback \u001b[38;5;241m=\u001b[39m ModelCheckpoint(filepath\u001b[38;5;241m=\u001b[39mcheckpoint_path, monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_loss\u001b[39m\u001b[38;5;124m'\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, save_best_only\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 38\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[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m64\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcheckpoint_callback\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:118\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    116\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    117\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 118\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    119\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    120\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;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:323\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    321\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    322\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[1;32m--> 323\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    324\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(\n\u001b[0;32m    325\u001b[0m         step, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pythonify_logs(logs)\n\u001b[0;32m    326\u001b[0m     )\n\u001b[0;32m    327\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_training:\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\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;32mc:\\Users\\jerin\\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;32mc:\\Users\\jerin\\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;32mc:\\Users\\jerin\\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;32mc:\\Users\\jerin\\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;32mc:\\Users\\jerin\\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;32mc:\\Users\\jerin\\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;32mc:\\Users\\jerin\\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;32mc:\\Users\\jerin\\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": [
@@ -662,7 +642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -709,7 +689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -727,7 +707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -749,7 +729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -782,7 +762,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 267,
    "metadata": {},
    "outputs": [
     {
@@ -863,7 +843,7 @@
        "1                   86.7               NaN                NaN     NaN     NaN  "
       ]
      },
-     "execution_count": 19,
+     "execution_count": 267,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -886,7 +866,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 268,
    "metadata": {},
    "outputs": [
     {
@@ -907,7 +887,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 269,
    "metadata": {},
    "outputs": [
     {
@@ -916,7 +896,7 @@
        "[]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 269,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -936,7 +916,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 270,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -950,38 +930,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 271,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/3\n",
-      "\u001b[1m3219/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0036\n",
-      "Epoch 1: val_loss improved from inf to 0.00068, saving model to lstm_energy_01.keras\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 20ms/step - loss: 0.0036 - val_loss: 6.8049e-04\n",
-      "Epoch 2/3\n",
-      "\u001b[1m3219/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 4.8055e-04\n",
-      "Epoch 2: val_loss improved from 0.00068 to 0.00064, saving model to lstm_energy_01.keras\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m60s\u001b[0m 19ms/step - loss: 4.8055e-04 - val_loss: 6.4225e-04\n",
-      "Epoch 3/3\n",
-      "\u001b[1m3219/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 4.6623e-04\n",
-      "Epoch 3: val_loss improved from 0.00064 to 0.00061, saving model to lstm_energy_01.keras\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 19ms/step - loss: 4.6622e-04 - val_loss: 6.0579e-04\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<keras.src.callbacks.history.History at 0x1ea4d2ed650>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train,test = traindataset,testdataset\n",
     "\n",
@@ -1016,7 +967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1026,25 +977,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 272,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m6345/6345\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 3ms/step\n"
+      "\u001b[1m6345/6345\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 6ms/step\n"
      ]
     }
    ],
    "source": [
+    "from tensorflow.keras.models import load_model\n",
+    "checkpoint_path = \"lstm_energy_01.keras\"\n",
+    "model = load_model(checkpoint_path)\n",
     "test_predict1 = model.predict(X_test)\n",
     "# train_predict1 = model.predict(X_train)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 273,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1065,7 +1019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1083,7 +1037,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1106,7 +1060,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1128,7 +1082,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1150,7 +1104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1159,7 +1113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1181,7 +1135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 325,
    "metadata": {},
    "outputs": [
     {
@@ -1207,18 +1161,18 @@
        "      <th></th>\n",
        "      <th>date</th>\n",
        "      <th>hp_hws_temp</th>\n",
+       "      <th>rtu_001_sat_sp_tn</th>\n",
+       "      <th>rtu_002_sat_sp_tn</th>\n",
        "      <th>rtu_003_sat_sp_tn</th>\n",
+       "      <th>rtu_004_sat_sp_tn</th>\n",
+       "      <th>rtu_001_fltrd_sa_flow_tn</th>\n",
+       "      <th>rtu_002_fltrd_sa_flow_tn</th>\n",
        "      <th>rtu_003_fltrd_sa_flow_tn</th>\n",
-       "      <th>rtu_003_sa_temp</th>\n",
-       "      <th>rtu_003_pa_static_stpt_tn</th>\n",
-       "      <th>rtu_003_oa_flow_tn</th>\n",
-       "      <th>rtu_003_oadmpr_pct</th>\n",
-       "      <th>rtu_003_econ_stpt_tn</th>\n",
-       "      <th>rtu_003_ra_temp</th>\n",
+       "      <th>rtu_004_fltrd_sa_flow_tn</th>\n",
        "      <th>...</th>\n",
-       "      <th>rtu_003_rf_vfd_spd_fbk_tn</th>\n",
-       "      <th>rtu_003_fltrd_gnd_lvl_plenum_press_tn</th>\n",
+       "      <th>rtu_002_fltrd_lvl2_plenum_press_tn</th>\n",
        "      <th>rtu_003_fltrd_lvl2_plenum_press_tn</th>\n",
+       "      <th>rtu_004_fltrd_lvl2_plenum_press_tn</th>\n",
        "      <th>wifi_third_south</th>\n",
        "      <th>wifi_fourth_south</th>\n",
        "      <th>air_temp_set_1</th>\n",
@@ -1233,18 +1187,18 @@
        "      <th>0</th>\n",
        "      <td>2018-01-01 00:00:00</td>\n",
        "      <td>75.3</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>70.0</td>\n",
        "      <td>65.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>14131.449</td>\n",
+       "      <td>13998.757</td>\n",
        "      <td>13558.539</td>\n",
-       "      <td>65.5</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>34.6</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>67.9</td>\n",
+       "      <td>9265.604</td>\n",
        "      <td>...</td>\n",
-       "      <td>49.9</td>\n",
-       "      <td>0.04</td>\n",
        "      <td>0.05</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.050</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>11.64</td>\n",
@@ -1257,18 +1211,18 @@
        "      <th>1</th>\n",
        "      <td>2018-01-01 00:01:00</td>\n",
        "      <td>75.3</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>70.0</td>\n",
        "      <td>65.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>14164.429</td>\n",
+       "      <td>14065.259</td>\n",
        "      <td>13592.909</td>\n",
-       "      <td>65.6</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>5992.059572</td>\n",
-       "      <td>34.6</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>67.9</td>\n",
+       "      <td>9265.604</td>\n",
        "      <td>...</td>\n",
-       "      <td>49.4</td>\n",
-       "      <td>0.04</td>\n",
+       "      <td>0.05</td>\n",
        "      <td>0.04</td>\n",
+       "      <td>0.046</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>11.64</td>\n",
@@ -1279,29 +1233,29 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>2 rows × 23 columns</p>\n",
+       "<p>2 rows × 65 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                  date  hp_hws_temp  rtu_003_sat_sp_tn  \\\n",
-       "0  2018-01-01 00:00:00         75.3               65.0   \n",
-       "1  2018-01-01 00:01:00         75.3               65.0   \n",
+       "                  date  hp_hws_temp  rtu_001_sat_sp_tn  rtu_002_sat_sp_tn  \\\n",
+       "0  2018-01-01 00:00:00         75.3               68.0               70.0   \n",
+       "1  2018-01-01 00:01:00         75.3               68.0               70.0   \n",
        "\n",
-       "   rtu_003_fltrd_sa_flow_tn  rtu_003_sa_temp  rtu_003_pa_static_stpt_tn  \\\n",
-       "0                 13558.539             65.5                        0.6   \n",
-       "1                 13592.909             65.6                        0.6   \n",
+       "   rtu_003_sat_sp_tn  rtu_004_sat_sp_tn  rtu_001_fltrd_sa_flow_tn  \\\n",
+       "0               65.0               69.0                 14131.449   \n",
+       "1               65.0               69.0                 14164.429   \n",
        "\n",
-       "   rtu_003_oa_flow_tn  rtu_003_oadmpr_pct  rtu_003_econ_stpt_tn  \\\n",
-       "0            0.000000                34.6                  65.0   \n",
-       "1         5992.059572                34.6                  65.0   \n",
+       "   rtu_002_fltrd_sa_flow_tn  rtu_003_fltrd_sa_flow_tn  \\\n",
+       "0                 13998.757                 13558.539   \n",
+       "1                 14065.259                 13592.909   \n",
        "\n",
-       "   rtu_003_ra_temp  ...  rtu_003_rf_vfd_spd_fbk_tn  \\\n",
-       "0             67.9  ...                       49.9   \n",
-       "1             67.9  ...                       49.4   \n",
+       "   rtu_004_fltrd_sa_flow_tn  ...  rtu_002_fltrd_lvl2_plenum_press_tn  \\\n",
+       "0                  9265.604  ...                                0.05   \n",
+       "1                  9265.604  ...                                0.05   \n",
        "\n",
-       "   rtu_003_fltrd_gnd_lvl_plenum_press_tn  rtu_003_fltrd_lvl2_plenum_press_tn  \\\n",
-       "0                                   0.04                                0.05   \n",
-       "1                                   0.04                                0.04   \n",
+       "   rtu_003_fltrd_lvl2_plenum_press_tn  rtu_004_fltrd_lvl2_plenum_press_tn  \\\n",
+       "0                                0.05                               0.050   \n",
+       "1                                0.04                               0.046   \n",
        "\n",
        "   wifi_third_south  wifi_fourth_south  air_temp_set_1  air_temp_set_2  \\\n",
        "0               NaN                NaN           11.64           11.51   \n",
@@ -1315,20 +1269,19 @@
        "0                   86.7  \n",
        "1                   86.7  \n",
        "\n",
-       "[2 rows x 23 columns]"
+       "[2 rows x 65 columns]"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 325,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "rtu = [\"rtu_003\"]\n",
+    "rtu = [\"rtu_003\",\"rtu_004\",\"rtu_001\",\"rtu_002\"]\n",
     "# wing = [\"hvac_N\",\"hvac_S\"]\n",
     "env = [\"air_temp_set_1\",\"air_temp_set_2\",\"dew_point_temperature_set_1d\",\"relative_humidity_set_1\",\"solar_radiation_set_1\"]\n",
     "wifi=[\"wifi_third_south\",\"wifi_fourth_south\"]\n",
-    "[\"rtu_003_ma_temp\",]\n",
     "# any(sub in col for sub in zone) or\n",
     "energy_data = merged[[\"date\",\"hp_hws_temp\"]+[col for col in merged.columns if \n",
     "                               any(sub in col for sub in rtu) or any(sub in col for sub in wifi)]+env]\n",
@@ -1341,23 +1294,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 363,
    "metadata": {},
    "outputs": [],
    "source": [
     "df_filtered['date'] = pd.to_datetime(df_filtered['date'], format = \"%Y-%m-%d %H:%M:%S\")\n",
-    "df_filtered = df_filtered[ (df_filtered.date.dt.date >date(2019, 4, 1)) & (df_filtered.date.dt.date< date(2020, 2, 15))]\n",
+    "df_filtered = df_filtered[ (df_filtered.date.dt.date >date(2018, 5, 1)) & (df_filtered.date.dt.date< date(2020, 5, 1))] #(2018, 5, 1)\n",
     "# df_filtered.isna().sum()\n",
     "if df_filtered.isna().any().any():\n",
-    "    print(\"There are NA values in the DataFrame columns.\")"
+    "    print(\"There are NA values in the DataFrame columns.\",df_filtered.isna().sum().tolist())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 364,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# df_filtered = df_filtered.loc[:,['date','hp_hws_temp',\n",
+    "#  'rtu_003_sa_temp',\n",
+    "#  'rtu_003_oadmpr_pct',\n",
+    "#  'rtu_003_ra_temp',\n",
+    "#  'rtu_003_oa_temp',\n",
+    "#  'rtu_003_ma_temp',\n",
+    "#  'rtu_003_sf_vfd_spd_fbk_tn',\n",
+    "#  'rtu_003_rf_vfd_spd_fbk_tn',\n",
+    "#  'air_temp_set_1',\n",
+    "#  'air_temp_set_2',\n",
+    "#  'dew_point_temperature_set_1d',\n",
+    "#  'relative_humidity_set_1',\n",
+    "#  'solar_radiation_set_1']]\n",
+    "\n",
     "df_filtered = df_filtered.loc[:,['date','hp_hws_temp',\n",
     " 'rtu_003_sa_temp',\n",
     " 'rtu_003_oadmpr_pct',\n",
@@ -1365,8 +1332,28 @@
     " 'rtu_003_oa_temp',\n",
     " 'rtu_003_ma_temp',\n",
     " 'rtu_003_sf_vfd_spd_fbk_tn',\n",
-    " 'rtu_003_rf_vfd_spd_fbk_tn','wifi_third_south',\n",
-    " 'wifi_fourth_south',\n",
+    " 'rtu_003_rf_vfd_spd_fbk_tn',\n",
+    " 'rtu_004_sa_temp',\n",
+    " 'rtu_004_oadmpr_pct',\n",
+    " 'rtu_004_ra_temp',\n",
+    " 'rtu_004_oa_temp',\n",
+    " 'rtu_004_ma_temp',\n",
+    " 'rtu_004_sf_vfd_spd_fbk_tn',\n",
+    " 'rtu_004_rf_vfd_spd_fbk_tn',\n",
+    " 'rtu_001_sa_temp',\n",
+    " 'rtu_001_oadmpr_pct',\n",
+    " 'rtu_001_ra_temp',\n",
+    " 'rtu_001_oa_temp',\n",
+    " 'rtu_001_ma_temp',\n",
+    " 'rtu_001_sf_vfd_spd_fbk_tn',\n",
+    " 'rtu_001_rf_vfd_spd_fbk_tn',\n",
+    " 'rtu_002_sa_temp',\n",
+    " 'rtu_002_oadmpr_pct',\n",
+    " 'rtu_002_ra_temp',\n",
+    " 'rtu_002_oa_temp',\n",
+    " 'rtu_002_ma_temp',\n",
+    " 'rtu_002_sf_vfd_spd_fbk_tn',\n",
+    " 'rtu_002_rf_vfd_spd_fbk_tn',\n",
     " 'air_temp_set_1',\n",
     " 'air_temp_set_2',\n",
     " 'dew_point_temperature_set_1d',\n",
@@ -1376,39 +1363,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 365,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 188,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n",
+      "[]\n"
+     ]
     }
    ],
    "source": [
-    "testdataset_df = df_filtered[(df_filtered.date.dt.date <date(2019, 8, 21))]\n",
+    "df_filtered = df_filtered.dropna()\n",
+    "# df_filtered.to_csv(\"sample_data.csv\",index=False)\n",
     "\n",
-    "traindataset_df = df_filtered[ (df_filtered.date.dt.date >date(2019, 11, 8))]\n",
-    "# .ewm(com = 1000,adjust=True).mean()\n",
-    "testdataset = testdataset_df.drop(columns=[\"date\"]).values\n",
+    "#----------------------------------------------------------------------------------\n",
+    "# testdataset_df = df_filtered[(df_filtered.date.dt.date >date(2019, 7, 21))]\n",
     "\n",
-    "traindataset = traindataset_df.drop(columns=[\"date\"]).values\n",
+    "# traindataset_df = df_filtered[(df_filtered.date.dt.date <date(2019, 7, 21))]\n",
     "\n",
-    "columns_with_na = traindataset_df.columns[traindataset_df.isna().any()].tolist()\n",
-    "columns_with_na"
+    "testdataset_df = df_filtered[(df_filtered.date.dt.date >date(2020, 1, 1))]\n",
+    "\n",
+    "traindataset_df = df_filtered[(df_filtered.date.dt.date <date(2020, 1, 1))]\n",
+    "\n",
+    "\n",
+    "# testdataset_df.to_csv(\"sample_data_02.csv\",index=False)\n",
+    "# .ewm(com = 1000,adjust=True).mean()\n",
+    "testdataset = testdataset_df.drop(columns=[\"date\"]).rolling(window=30,min_periods=1).mean()\n",
+    "\n",
+    "traindataset = traindataset_df.drop(columns=[\"date\"]).rolling(window=30,min_periods=1).mean().values\n",
+    "# testdataset.to_csv(\"sample_data_smooth_01.csv\",index=False)\n",
+    "columns_with_na_train = traindataset_df.columns[traindataset_df.isna().any()].tolist()\n",
+    "columns_with_na_test = testdataset_df.columns[testdataset_df.isna().any()].tolist()\n",
+    "print(columns_with_na_train)\n",
+    "print(columns_with_na_test)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 366,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but StandardScaler was fitted without feature names\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
+    "\n",
     "traindataset = traindataset.astype('float32')\n",
     "testdataset = testdataset.astype('float32')\n",
     "\n",
@@ -1419,16 +1427,120 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 250,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['scaler_1.pkl']"
+      ]
+     },
+     "execution_count": 250,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import joblib\n",
+    "# joblib.dump(scaler, 'scaler_1.pkl')\n",
+    "# loaded_scaler = joblib.load('scaler.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:205: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(**kwargs)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/5\n"
+      "Epoch 1/5\n",
+      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0568\n",
+      "Epoch 1: val_loss improved from inf to 0.08613, saving model to lstm_2rtu_smooth_02.keras\n",
+      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m198s\u001b[0m 24ms/step - loss: 0.0568 - val_loss: 0.0861\n",
+      "Epoch 2/5\n",
+      "\u001b[1m8066/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 9.6620e-04\n",
+      "Epoch 2: val_loss improved from 0.08613 to 0.07149, saving model to lstm_2rtu_smooth_02.keras\n",
+      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m189s\u001b[0m 23ms/step - loss: 9.6615e-04 - val_loss: 0.0715\n",
+      "Epoch 3/5\n",
+      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 5.0913e-04\n",
+      "Epoch 3: val_loss improved from 0.07149 to 0.06955, saving model to lstm_2rtu_smooth_02.keras\n",
+      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m187s\u001b[0m 23ms/step - loss: 5.0913e-04 - val_loss: 0.0695\n",
+      "Epoch 4/5\n",
+      "\u001b[1m8066/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 3.9422e-04\n",
+      "Epoch 4: val_loss improved from 0.06955 to 0.06820, saving model to lstm_2rtu_smooth_02.keras\n",
+      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m185s\u001b[0m 23ms/step - loss: 3.9421e-04 - val_loss: 0.0682\n",
+      "Epoch 5/5\n",
+      "\u001b[1m8066/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 3.6433e-04\n",
+      "Epoch 5: val_loss improved from 0.06820 to 0.06542, saving model to lstm_2rtu_smooth_02.keras\n",
+      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m188s\u001b[0m 23ms/step - loss: 3.6432e-04 - val_loss: 0.0654\n"
      ]
     },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x15088066a50>"
+      ]
+     },
+     "execution_count": 201,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#2 rtu model\n",
+    "\n",
+    "# train,test = traindataset,testdataset\n",
+    "\n",
+    "# def create_dataset(dataset,time_step):\n",
+    "#     x = [[] for _ in range(20)] \n",
+    "#     Y = []\n",
+    "#     for i in range(len(dataset) - time_step - 1):\n",
+    "#         for j in range(20):\n",
+    "#             x[j].append(dataset[i:(i + time_step), j])\n",
+    "#         Y.append([dataset[i + time_step, 0],dataset[i + time_step, 1],dataset[i + time_step, 2],dataset[i + time_step, 3],\n",
+    "#                   dataset[i + time_step, 4],dataset[i + time_step, 5],\n",
+    "#                   dataset[i + time_step, 6],dataset[i + time_step, 7],\n",
+    "#                   dataset[i + time_step, 8],dataset[i + time_step, 9],dataset[i + time_step, 10],\n",
+    "#                   dataset[i + time_step, 11],dataset[i + time_step, 12],\n",
+    "#                   dataset[i + time_step, 13],dataset[i + time_step, 14]])\n",
+    "#     x= [np.array(feature_list) for feature_list in x]\n",
+    "#     Y = np.reshape(Y,(len(Y),15))\n",
+    "#     return np.stack(x,axis=2),Y\n",
+    "\n",
+    "# time_step = 30\n",
+    "# X_train, y_train = create_dataset(train, time_step)\n",
+    "# X_test, y_test = create_dataset(test, time_step)\n",
+    "\n",
+    "\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=30))\n",
+    "# model.add(Dense(units=15))\n",
+    "\n",
+    "# model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "\n",
+    "# checkpoint_path = \"lstm_2rtu_smooth_02.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": 340,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
@@ -1441,34 +1553,35 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m3217/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.1413\n",
-      "Epoch 1: val_loss improved from inf to 0.52256, saving model to lstm_smooth_01.keras\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 20ms/step - loss: 0.1412 - val_loss: 0.5226\n",
+      "Epoch 1/5\n",
+      "\u001b[1m7132/7133\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.0395\n",
+      "Epoch 1: val_loss improved from inf to 0.06411, saving model to lstm_4rtu_smooth_04.keras\n",
+      "\u001b[1m7133/7133\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m247s\u001b[0m 34ms/step - loss: 0.0395 - val_loss: 0.0641\n",
       "Epoch 2/5\n",
-      "\u001b[1m3217/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0393\n",
-      "Epoch 2: val_loss improved from 0.52256 to 0.50228, saving model to lstm_smooth_01.keras\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 20ms/step - loss: 0.0393 - val_loss: 0.5023\n",
+      "\u001b[1m7131/7133\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.0012\n",
+      "Epoch 2: val_loss improved from 0.06411 to 0.04068, saving model to lstm_4rtu_smooth_04.keras\n",
+      "\u001b[1m7133/7133\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m248s\u001b[0m 35ms/step - loss: 0.0012 - val_loss: 0.0407\n",
       "Epoch 3/5\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0347\n",
-      "Epoch 3: val_loss improved from 0.50228 to 0.48711, saving model to lstm_smooth_01.keras\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 20ms/step - loss: 0.0347 - val_loss: 0.4871\n",
+      "\u001b[1m7133/7133\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 7.2848e-04\n",
+      "Epoch 3: val_loss improved from 0.04068 to 0.03509, saving model to lstm_4rtu_smooth_04.keras\n",
+      "\u001b[1m7133/7133\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m245s\u001b[0m 34ms/step - loss: 7.2848e-04 - val_loss: 0.0351\n",
       "Epoch 4/5\n",
-      "\u001b[1m3217/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0319\n",
-      "Epoch 4: val_loss did not improve from 0.48711\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 20ms/step - loss: 0.0319 - val_loss: 0.4958\n",
+      "\u001b[1m7132/7133\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 5.7519e-04\n",
+      "Epoch 4: val_loss did not improve from 0.03509\n",
+      "\u001b[1m7133/7133\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m245s\u001b[0m 34ms/step - loss: 5.7520e-04 - val_loss: 0.0400\n",
       "Epoch 5/5\n",
-      "\u001b[1m3218/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0303\n",
-      "Epoch 5: val_loss did not improve from 0.48711\n",
-      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 21ms/step - loss: 0.0303 - val_loss: 0.5026\n"
+      "\u001b[1m7132/7133\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 5.3138e-04\n",
+      "Epoch 5: val_loss did not improve from 0.03509\n",
+      "\u001b[1m7133/7133\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m244s\u001b[0m 34ms/step - loss: 5.3136e-04 - val_loss: 0.0357\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<keras.src.callbacks.history.History at 0x1d460bb83d0>"
+       "<keras.src.callbacks.history.History at 0x229f1af02d0>"
       ]
      },
-     "execution_count": 191,
+     "execution_count": 340,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1477,15 +1590,25 @@
     "train,test = traindataset,testdataset\n",
     "\n",
     "def create_dataset(dataset,time_step):\n",
-    "    x = [[] for _ in range(15)] \n",
+    "    x = [[] for _ in range(34)] \n",
     "    Y = []\n",
     "    for i in range(len(dataset) - time_step - 1):\n",
-    "        for j in range(15):\n",
+    "        for j in range(34):\n",
     "            x[j].append(dataset[i:(i + time_step), j])\n",
-    "        Y.append([dataset[i + time_step, 0],dataset[i + time_step, 1],dataset[i + time_step, 2],dataset[i + time_step, 3],dataset[i + time_step, 4],dataset[i + time_step, 5],\n",
-    "                  dataset[i + time_step, 6],dataset[i + time_step, 7]])\n",
+    "        Y.append([dataset[i + time_step, 0],dataset[i + time_step, 1],dataset[i + time_step, 2],dataset[i + time_step, 3],\n",
+    "                  dataset[i + time_step, 4],dataset[i + time_step, 5],\n",
+    "                  dataset[i + time_step, 6],dataset[i + time_step, 7],\n",
+    "                  dataset[i + time_step, 8],dataset[i + time_step, 9],dataset[i + time_step, 10],\n",
+    "                  dataset[i + time_step, 11],dataset[i + time_step, 12],\n",
+    "                  dataset[i + time_step, 13],dataset[i + time_step, 14],\n",
+    "                  dataset[i + time_step, 15],dataset[i + time_step, 16],dataset[i + time_step, 17],\n",
+    "                  dataset[i + time_step, 18],dataset[i + time_step, 19],\n",
+    "                  dataset[i + time_step, 20],dataset[i + time_step, 21],\n",
+    "                  dataset[i + time_step, 22],dataset[i + time_step, 23],dataset[i + time_step, 24],\n",
+    "                  dataset[i + time_step, 24],dataset[i + time_step, 26],\n",
+    "                  dataset[i + time_step, 27],dataset[i + time_step, 28]])\n",
     "    x= [np.array(feature_list) for feature_list in x]\n",
-    "    Y = np.reshape(Y,(len(Y),8))\n",
+    "    Y = np.reshape(Y,(len(Y),29))\n",
     "    return np.stack(x,axis=2),Y\n",
     "\n",
     "time_step = 30\n",
@@ -1494,97 +1617,135 @@
     "\n",
     "\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=30))\n",
-    "model.add(Dense(units=8))\n",
+    "model.add(LSTM(units=80, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
+    "model.add(LSTM(units=60, return_sequences=True))\n",
+    "model.add(LSTM(units=60))\n",
+    "model.add(Dense(units=29))\n",
     "\n",
     "model.compile(optimizer='adam', loss='mean_squared_error')\n",
     "\n",
-    "checkpoint_path = \"lstm_smooth_01.keras\"\n",
+    "checkpoint_path = \"lstm_4rtu_smooth_04.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": 192,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m6345/6345\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 4ms/step\n"
+      "Epoch 1/5\n",
+      "\u001b[1m8061/8061\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 8.9528e-04\n",
+      "Epoch 1: val_loss did not improve from 0.19006\n",
+      "\u001b[1m8061/8061\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m239s\u001b[0m 30ms/step - loss: 8.9529e-04 - val_loss: 0.2020\n",
+      "Epoch 2/5\n",
+      "\u001b[1m8060/8061\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 8.0121e-04\n",
+      "Epoch 2: val_loss improved from 0.19006 to 0.18934, saving model to lstm_4rtu_smooth_03.keras\n",
+      "\u001b[1m8061/8061\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m234s\u001b[0m 29ms/step - loss: 8.0122e-04 - val_loss: 0.1893\n",
+      "Epoch 3/5\n",
+      "\u001b[1m8061/8061\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 9.8454e-04\n",
+      "Epoch 3: val_loss did not improve from 0.18934\n",
+      "\u001b[1m8061/8061\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m231s\u001b[0m 29ms/step - loss: 9.8453e-04 - val_loss: 0.2004\n",
+      "Epoch 4/5\n",
+      "\u001b[1m8060/8061\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 7.8113e-04\n",
+      "Epoch 4: val_loss did not improve from 0.18934\n",
+      "\u001b[1m8061/8061\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m232s\u001b[0m 29ms/step - loss: 7.8114e-04 - val_loss: 0.2031\n",
+      "Epoch 5/5\n",
+      "\u001b[1m8059/8061\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 8.8365e-04\n",
+      "Epoch 5: val_loss did not improve from 0.18934\n",
+      "\u001b[1m8061/8061\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m234s\u001b[0m 29ms/step - loss: 8.8360e-04 - val_loss: 0.1915\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x22477217090>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "test_predict1 = model.predict(X_test)"
+    "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": 193,
+   "execution_count": 284,
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib qt\n",
-    "var = 0\n",
-    "plt.plot(y_test[:,var], label='Original Testing Data', color='blue')\n",
-    "plt.plot(test_predict1[:,var], label='Predicted Testing Data', color='red',alpha=0.8)\n",
-    "anomalies = np.where(abs(test_predict1[:,var] - y_test[:,var]) > 0.38)[var]\n",
-    "plt.scatter(anomalies,test_predict1[anomalies,var], color='black',marker =\"o\",s=100 )\n",
-    "\n",
-    "\n",
-    "plt.title('Testing Data - Predicted vs Actual')\n",
-    "plt.xlabel('Time')\n",
-    "plt.ylabel('Value')\n",
-    "plt.legend()\n",
-    "plt.show()"
+    "from tensorflow.keras.models import load_model\n",
+    "checkpoint_path = \"lstm_4rtu_smooth_02.keras\"\n",
+    "model = load_model(checkpoint_path)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 341,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1d334841450>]"
-      ]
-     },
-     "execution_count": 176,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m11765/11765\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 7ms/step\n"
+     ]
     }
    ],
    "source": [
-    "var = 0\n",
-    "plt.plot((test_predict1 - y_test)[:,var])"
+    "test_predict1 = model.predict(X_test)\n",
+    "# train_predict1 = model.predict(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 362,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib qt\n",
+    "var = 13\n",
+    "plt.plot(y_test[:,var], label='Original Testing Data', color='blue')\n",
+    "plt.plot(test_predict2[:,var], label='Predicted Testing Data', color='red',alpha=0.8)\n",
+    "# anomalies = np.where(abs(test_predict1[:,var] - y_test[:,var]) > 0.38)\n",
+    "# plt.scatter(anomalies,test_predict1[anomalies,var], color='black',marker =\"o\",s=100 )\n",
+    "\n",
+    "\n",
+    "plt.title('Testing Data - Predicted vs Actual')\n",
+    "plt.xlabel('Time')\n",
+    "plt.ylabel('Value')\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
-    "params = ['hp_hws_temp',\n",
+    "params = [\n",
     " 'rtu_003_sa_temp',\n",
     " 'rtu_003_oadmpr_pct',\n",
     " 'rtu_003_ra_temp',\n",
     " 'rtu_003_oa_temp',\n",
     " 'rtu_003_ma_temp',\n",
     " 'rtu_003_sf_vfd_spd_fbk_tn',\n",
-    " 'rtu_003_rf_vfd_spd_fbk_tn','wifi_third_south',\n",
-    " 'wifi_fourth_south',\n",
-    " 'air_temp_set_1',\n",
-    " 'air_temp_set_2',\n",
-    " 'dew_point_temperature_set_1d',\n",
-    " 'relative_humidity_set_1',\n",
-    " 'solar_radiation_set_1']\n",
+    " 'rtu_003_rf_vfd_spd_fbk_tn']\n",
     "\n",
     "idx_2_params = {}\n",
     "for i, param in enumerate(params):\n",
@@ -1593,30 +1754,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{0: 'hp_hws_temp',\n",
-       " 1: 'rtu_003_sa_temp',\n",
-       " 2: 'rtu_003_oadmpr_pct',\n",
-       " 3: 'rtu_003_ra_temp',\n",
-       " 4: 'rtu_003_oa_temp',\n",
-       " 5: 'rtu_003_ma_temp',\n",
-       " 6: 'rtu_003_sf_vfd_spd_fbk_tn',\n",
-       " 7: 'rtu_003_rf_vfd_spd_fbk_tn',\n",
-       " 8: 'wifi_third_south',\n",
-       " 9: 'wifi_fourth_south',\n",
-       " 10: 'air_temp_set_1',\n",
-       " 11: 'air_temp_set_2',\n",
-       " 12: 'dew_point_temperature_set_1d',\n",
-       " 13: 'relative_humidity_set_1',\n",
-       " 14: 'solar_radiation_set_1'}"
+       "{0: 'rtu_003_sa_temp',\n",
+       " 1: 'rtu_003_oadmpr_pct',\n",
+       " 2: 'rtu_003_ra_temp',\n",
+       " 3: 'rtu_003_oa_temp',\n",
+       " 4: 'rtu_003_ma_temp',\n",
+       " 5: 'rtu_003_sf_vfd_spd_fbk_tn',\n",
+       " 6: 'rtu_003_rf_vfd_spd_fbk_tn'}"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1634,62 +1787,609 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.cluster import KMeans\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.decomposition import PCA\n",
-    "# Generating random data for demonstration\n",
-    "np.random.seed(0)\n",
-    "X = test_predict1 - y_test\n",
-    "\n",
-    "pca = PCA(n_components=2)\n",
-    "X = pca.fit_transform(X)\n",
-    "\n",
-    "\n",
-    "k = 3\n",
-    "\n",
-    "kmeans = KMeans(n_clusters=k)\n",
-    "\n",
-    "kmeans.fit(X)\n",
-    "\n",
-    "# Getting the cluster centers and labels\n",
-    "centroids = kmeans.cluster_centers_\n",
-    "labels = kmeans.labels_\n",
-    "\n",
-    "# Plotting the data points and cluster centers\n",
-    "plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', alpha=0.5, edgecolors='k')\n",
-    "plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', c='red', s=200, linewidths=2)\n",
-    "plt.title('KMeans Clustering')\n",
-    "plt.xlabel('Feature 1')\n",
-    "plt.ylabel('Feature 2')\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 195,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1d33ccad250>]"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plt.plot((test_predict1 - y_test)[:,2])"
-   ]
-  },
-  {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=1, random_state=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=1, random_state=10)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=1, random_state=10)"
+      ]
+     },
+     "execution_count": 195,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "np.random.seed(0)\n",
+    "X1 = train_predict1[:,1:8] - y_train[:,1:8]\n",
+    "X2 = train_predict1[:,8:15] - y_train[:,8:15]\n",
+    "X3 = train_predict1[:,15:22] - y_train[:,15:22]\n",
+    "X4 = train_predict1[:,22:29] - y_train[:,22:29]\n",
+    "\n",
+    "k = 1\n",
+    "\n",
+    "kmeans1 = KMeans(n_clusters=k, random_state=10) #2 for without smoothing\n",
+    "kmeans1.fit(X1)\n",
+    "kmeans2 = KMeans(n_clusters=k, random_state=10) #2 for without smoothing\n",
+    "kmeans2.fit(X2)\n",
+    "kmeans3 = KMeans(n_clusters=k, random_state=10) #2 for without smoothing\n",
+    "kmeans3.fit(X3)\n",
+    "kmeans4 = KMeans(n_clusters=k, random_state=10) #2 for without smoothing\n",
+    "kmeans4.fit(X4)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# pca = PCA(n_components=2)\n",
+    "# X = pca.fit_transform(X2)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# # Getting the cluster centers and labels\n",
+    "# centroids = pca.transform(kmeans.cluster_centers_)\n",
+    "# labels = kmeans.labels_\n",
+    "# print(kmeans.cluster_centers_)\n",
+    "# Plotting the data points and cluster centers\n",
+    "# plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', alpha=0.5)\n",
+    "# plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', c='red', s=200, linewidths=2)\n",
+    "# plt.title('KMeans Clustering')\n",
+    "# plt.xlabel('Feature 1')\n",
+    "# plt.ylabel('Feature 2')\n",
+    "# plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 249,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['kmeans_model4.pkl']"
+      ]
+     },
+     "execution_count": 249,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import joblib\n",
+    "# joblib.dump(kmeans4, 'kmeans_model4.pkl')\n",
+    "# joblib.dump(pca, 'pca_model.pkl')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['rtu_003_oadmpr_pct',\n",
+       " 'rtu_003_oa_temp',\n",
+       " 'rtu_003_ra_temp',\n",
+       " 'rtu_003_rf_vfd_spd_fbk_tn',\n",
+       " 'rtu_003_sf_vfd_spd_fbk_tn',\n",
+       " 'rtu_003_ma_temp',\n",
+       " 'rtu_003_sa_temp']"
+      ]
+     },
+     "execution_count": 196,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[ params[i] for i in np.flip(np.argsort(abs(kmeans1.cluster_centers_[0])))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 323,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x228993bb690>]"
+      ]
+     },
+     "execution_count": 323,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%matplotlib qt\n",
+    "\n",
+    "distance1 = np.linalg.norm((test_predict1[:,1:8]-y_test[:,1:8])-kmeans1.cluster_centers_[0], ord=2, axis = 1)\n",
+    "distance2 = np.linalg.norm((test_predict1[:,8:15]-y_test[:,8:15])-kmeans2.cluster_centers_[0], ord=2, axis = 1)\n",
+    "distance3 = np.linalg.norm((test_predict1[:,15:22]-y_test[:,15:22])-kmeans3.cluster_centers_[0], ord=2, axis = 1)\n",
+    "distance4 = np.linalg.norm((test_predict1[:,22:29]-y_test[:,22:29])-kmeans4.cluster_centers_[0], ord=2, axis = 1)\n",
+    "# distance_a = np.linalg.norm(test_predict1[:,8:]-a, ord=2, axis = 1)\n",
+    "# plt.plot(y_test[:,23],alpha=0.6)\n",
+    "plt.plot(y_test[:,3],alpha=0.6)\n",
+    "# plt.plot(y_test[:,6],alpha=0.6)\n",
+    "# plt.plot(y_test[:,7],alpha=0.6)\n",
+    "plt.plot(test_predict1[:,3],alpha=0.6)\n",
+    "plt.plot(abs(distance1)>2)\n",
+    "# plt.plot(abs(distance2)>3.5)\n",
+    "# plt.plot(abs(distance3)>5)\n",
+    "# plt.plot(abs(distance4)>5)\n",
+    "# plt.plot(distance_a>8,c='g')\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2292a1bde50>]"
+      ]
+     },
+     "execution_count": 277,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# fault = []\n",
+    "# for i in range(60,len(distance4)):\n",
+    "#     if np.mean((abs(distance4)>5)[i-60:i])>0.60 :\n",
+    "#         fault.append(1)\n",
+    "#     else:\n",
+    "#         fault.append(0)\n",
+    "plt.plot(fault,c='r')\n",
+    "plt.plot(abs(distance4)>5,c='b')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x227020e5bd0>]"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test[:,23]\n",
+    "arr = test[:, 23]\n",
+    "condition = (arr < 5) & (np.arange(len(arr)) < 35000) & (np.arange(len(arr)) > 30000)\n",
+    "arr[condition] = 2\n",
+    "plt.plot(test[:,23])\n"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 77,
    "metadata": {},
@@ -1715,7 +2415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1723,7 +2423,7 @@
     "X= test_predict1 - y_test\n",
     "processed_data = []\n",
     "feat_df = pd.DataFrame(columns=[\"mean\",\"std\",])\n",
-    "for i in range(0,len(X), 30 ):\n",
+    "for i in range(0,len(X), 40 ):\n",
     "    mean = X[i:i+k].mean(axis = 0)\n",
     "    std = X[i:i+k].std(axis = 0)\n",
     "    max = X[i:i+k].max(axis = 0)\n",
@@ -1736,24 +2436,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 38,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'processed_data' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[38], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mprocessed_data\u001b[49m\n\u001b[0;32m      4\u001b[0m k \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m      6\u001b[0m kmeans \u001b[38;5;241m=\u001b[39m KMeans(n_clusters\u001b[38;5;241m=\u001b[39mk,random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'processed_data' is not defined"
+     ]
+    }
+   ],
    "source": [
     "X = processed_data\n",
     "\n",
-    "pca = PCA(n_components=2)\n",
-    "X = pca.fit_transform(X)\n",
-    "\n",
     "\n",
-    "k = 4\n",
+    "k = 2\n",
     "\n",
-    "kmeans = KMeans(n_clusters=k)\n",
+    "kmeans = KMeans(n_clusters=k,random_state=4)\n",
     "\n",
     "kmeans.fit(X)\n",
     "\n",
-    "# Getting the cluster centers and labels\n",
-    "centroids = kmeans.cluster_centers_\n",
+    "pca = PCA(n_components=2)\n",
+    "X = pca.fit_transform(X)\n",
+    "\n",
+    "centroids = pca.transform(kmeans.cluster_centers_)\n",
     "labels = kmeans.labels_\n",
     "\n",
     "# Plotting the data points and cluster centers\n",
@@ -1767,416 +2478,127 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.76482953e-02, -4.84286715e-03],\n",
+       "       [ 2.35918490e+01,  6.47383719e+00]])"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans.cluster_centers_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "operands could not be broadcast together with shapes (481579,2) (15,) ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[123], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m distance \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinalg\u001b[38;5;241m.\u001b[39mnorm(\u001b[43mpca\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_predict1\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mkmeans\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcluster_centers_\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m, \u001b[38;5;28mord\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(y_test[:,\u001b[38;5;241m6\u001b[39m])\n\u001b[0;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(distance\u001b[38;5;241m>\u001b[39m\u001b[38;5;241m50\u001b[39m,c\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (481579,2) (15,) "
+     ]
+    }
+   ],
+   "source": [
+    "distance = np.linalg.norm(pca.transform(test_predict1)-kmeans.cluster_centers_[1], ord=2, axis = 1)\n",
+    "plt.plot(y_test[:,6])\n",
+    "plt.plot(distance>50,c='r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# from sklearn.mixture import GaussianMixture\n",
+    "# import numpy as np\n",
+    "# import matplotlib.pyplot as plt\n",
+    "\n",
+    "# X = processed_data\n",
+    "\n",
+    "\n",
+    "# gmm = GaussianMixture(n_components=2,random_state=10)\n",
+    "# gmm.fit(X)\n",
+    "# labels = gmm.predict(X)\n",
     "\n",
-    "dd = df_filtered.drop(columns=[\"date\"],inplace=False)\n",
-    "dg = dd.ewm(com = 1000,adjust=True).mean()"
+    "\n",
+    "# plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', alpha=0.5)\n",
+    "# plt.title('GMM Clustering')\n",
+    "# plt.xlabel('Feature 1')\n",
+    "# plt.ylabel('Feature 2')\n",
+    "# plt.show()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 297,
    "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>hp_hws_temp</th>\n",
-       "      <th>rtu_003_sa_temp</th>\n",
-       "      <th>rtu_003_oadmpr_pct</th>\n",
-       "      <th>rtu_003_ra_temp</th>\n",
-       "      <th>rtu_003_oa_temp</th>\n",
-       "      <th>rtu_003_ma_temp</th>\n",
-       "      <th>rtu_003_sf_vfd_spd_fbk_tn</th>\n",
-       "      <th>rtu_003_rf_vfd_spd_fbk_tn</th>\n",
-       "      <th>wifi_third_south</th>\n",
-       "      <th>wifi_fourth_south</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",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>555845</th>\n",
-       "      <td>2019-04-02 00:00:00</td>\n",
-       "      <td>120.7</td>\n",
-       "      <td>66.7</td>\n",
-       "      <td>84.4</td>\n",
-       "      <td>72.2</td>\n",
-       "      <td>59.2</td>\n",
-       "      <td>66.7</td>\n",
-       "      <td>79.6</td>\n",
-       "      <td>53.7</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>15.67</td>\n",
-       "      <td>14.92</td>\n",
-       "      <td>11.77</td>\n",
-       "      <td>77.80</td>\n",
-       "      <td>147.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>555846</th>\n",
-       "      <td>2019-04-02 00:01:00</td>\n",
-       "      <td>120.4</td>\n",
-       "      <td>65.8</td>\n",
-       "      <td>85.4</td>\n",
-       "      <td>72.2</td>\n",
-       "      <td>59.5</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>78.1</td>\n",
-       "      <td>54.4</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>15.67</td>\n",
-       "      <td>14.92</td>\n",
-       "      <td>11.77</td>\n",
-       "      <td>77.80</td>\n",
-       "      <td>147.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>555847</th>\n",
-       "      <td>2019-04-02 00:02:00</td>\n",
-       "      <td>120.1</td>\n",
-       "      <td>65.1</td>\n",
-       "      <td>66.2</td>\n",
-       "      <td>72.1</td>\n",
-       "      <td>59.4</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>60.1</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>15.67</td>\n",
-       "      <td>14.92</td>\n",
-       "      <td>11.77</td>\n",
-       "      <td>77.80</td>\n",
-       "      <td>147.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>555848</th>\n",
-       "      <td>2019-04-02 00:03:00</td>\n",
-       "      <td>119.6</td>\n",
-       "      <td>64.9</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>72.2</td>\n",
-       "      <td>59.4</td>\n",
-       "      <td>65.7</td>\n",
-       "      <td>79.1</td>\n",
-       "      <td>55.5</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>15.67</td>\n",
-       "      <td>14.92</td>\n",
-       "      <td>11.77</td>\n",
-       "      <td>77.80</td>\n",
-       "      <td>147.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>555849</th>\n",
-       "      <td>2019-04-02 00:04:00</td>\n",
-       "      <td>119.3</td>\n",
-       "      <td>65.5</td>\n",
-       "      <td>54.6</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>59.2</td>\n",
-       "      <td>67.1</td>\n",
-       "      <td>75.2</td>\n",
-       "      <td>53.1</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>15.67</td>\n",
-       "      <td>14.92</td>\n",
-       "      <td>11.77</td>\n",
-       "      <td>77.80</td>\n",
-       "      <td>147.1</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\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>1080190</th>\n",
-       "      <td>2020-02-14 23:57:00</td>\n",
-       "      <td>121.9</td>\n",
-       "      <td>67.3</td>\n",
-       "      <td>52.8</td>\n",
-       "      <td>73.2</td>\n",
-       "      <td>63.5</td>\n",
-       "      <td>69.2</td>\n",
-       "      <td>80.9</td>\n",
-       "      <td>61.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>16.42</td>\n",
-       "      <td>13.93</td>\n",
-       "      <td>6.93</td>\n",
-       "      <td>53.66</td>\n",
-       "      <td>347.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1080191</th>\n",
-       "      <td>2020-02-14 23:58:00</td>\n",
-       "      <td>122.7</td>\n",
-       "      <td>69.2</td>\n",
-       "      <td>64.8</td>\n",
-       "      <td>73.3</td>\n",
-       "      <td>63.4</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>53.8</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>16.42</td>\n",
-       "      <td>13.93</td>\n",
-       "      <td>6.93</td>\n",
-       "      <td>53.66</td>\n",
-       "      <td>347.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1080192</th>\n",
-       "      <td>2020-02-14 23:58:00</td>\n",
-       "      <td>122.7</td>\n",
-       "      <td>69.2</td>\n",
-       "      <td>64.8</td>\n",
-       "      <td>73.3</td>\n",
-       "      <td>63.4</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>53.8</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>16.42</td>\n",
-       "      <td>13.93</td>\n",
-       "      <td>6.93</td>\n",
-       "      <td>53.66</td>\n",
-       "      <td>347.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1080193</th>\n",
-       "      <td>2020-02-14 23:59:00</td>\n",
-       "      <td>122.9</td>\n",
-       "      <td>68.7</td>\n",
-       "      <td>80.8</td>\n",
-       "      <td>73.3</td>\n",
-       "      <td>63.1</td>\n",
-       "      <td>67.3</td>\n",
-       "      <td>82.2</td>\n",
-       "      <td>60.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>16.42</td>\n",
-       "      <td>13.93</td>\n",
-       "      <td>6.93</td>\n",
-       "      <td>53.66</td>\n",
-       "      <td>347.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1080194</th>\n",
-       "      <td>2020-02-14 23:59:00</td>\n",
-       "      <td>122.9</td>\n",
-       "      <td>68.7</td>\n",
-       "      <td>80.8</td>\n",
-       "      <td>73.3</td>\n",
-       "      <td>63.1</td>\n",
-       "      <td>67.3</td>\n",
-       "      <td>82.2</td>\n",
-       "      <td>60.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>16.42</td>\n",
-       "      <td>13.93</td>\n",
-       "      <td>6.93</td>\n",
-       "      <td>53.66</td>\n",
-       "      <td>347.9</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>524350 rows × 16 columns</p>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "                       date  hp_hws_temp  rtu_003_sa_temp  rtu_003_oadmpr_pct  \\\n",
-       "555845  2019-04-02 00:00:00        120.7             66.7                84.4   \n",
-       "555846  2019-04-02 00:01:00        120.4             65.8                85.4   \n",
-       "555847  2019-04-02 00:02:00        120.1             65.1                66.2   \n",
-       "555848  2019-04-02 00:03:00        119.6             64.9                56.0   \n",
-       "555849  2019-04-02 00:04:00        119.3             65.5                54.6   \n",
-       "...                     ...          ...              ...                 ...   \n",
-       "1080190 2020-02-14 23:57:00        121.9             67.3                52.8   \n",
-       "1080191 2020-02-14 23:58:00        122.7             69.2                64.8   \n",
-       "1080192 2020-02-14 23:58:00        122.7             69.2                64.8   \n",
-       "1080193 2020-02-14 23:59:00        122.9             68.7                80.8   \n",
-       "1080194 2020-02-14 23:59:00        122.9             68.7                80.8   \n",
-       "\n",
-       "         rtu_003_ra_temp  rtu_003_oa_temp  rtu_003_ma_temp  \\\n",
-       "555845              72.2             59.2             66.7   \n",
-       "555846              72.2             59.5             65.0   \n",
-       "555847              72.1             59.4             64.0   \n",
-       "555848              72.2             59.4             65.7   \n",
-       "555849              72.0             59.2             67.1   \n",
-       "...                  ...              ...              ...   \n",
-       "1080190             73.2             63.5             69.2   \n",
-       "1080191             73.3             63.4             70.0   \n",
-       "1080192             73.3             63.4             70.0   \n",
-       "1080193             73.3             63.1             67.3   \n",
-       "1080194             73.3             63.1             67.3   \n",
-       "\n",
-       "         rtu_003_sf_vfd_spd_fbk_tn  rtu_003_rf_vfd_spd_fbk_tn  \\\n",
-       "555845                        79.6                       53.7   \n",
-       "555846                        78.1                       54.4   \n",
-       "555847                        78.0                       60.1   \n",
-       "555848                        79.1                       55.5   \n",
-       "555849                        75.2                       53.1   \n",
-       "...                            ...                        ...   \n",
-       "1080190                       80.9                       61.3   \n",
-       "1080191                       81.0                       53.8   \n",
-       "1080192                       81.0                       53.8   \n",
-       "1080193                       82.2                       60.1   \n",
-       "1080194                       82.2                       60.1   \n",
-       "\n",
-       "         wifi_third_south  wifi_fourth_south  air_temp_set_1  air_temp_set_2  \\\n",
-       "555845               34.0               31.0           15.67           14.92   \n",
-       "555846               34.0               31.0           15.67           14.92   \n",
-       "555847               34.0               31.0           15.67           14.92   \n",
-       "555848               34.0               31.0           15.67           14.92   \n",
-       "555849               34.0               31.0           15.67           14.92   \n",
-       "...                   ...                ...             ...             ...   \n",
-       "1080190               0.0                0.0           16.42           13.93   \n",
-       "1080191               0.0                0.0           16.42           13.93   \n",
-       "1080192               0.0                0.0           16.42           13.93   \n",
-       "1080193               0.0                0.0           16.42           13.93   \n",
-       "1080194               0.0                0.0           16.42           13.93   \n",
-       "\n",
-       "         dew_point_temperature_set_1d  relative_humidity_set_1  \\\n",
-       "555845                          11.77                    77.80   \n",
-       "555846                          11.77                    77.80   \n",
-       "555847                          11.77                    77.80   \n",
-       "555848                          11.77                    77.80   \n",
-       "555849                          11.77                    77.80   \n",
-       "...                               ...                      ...   \n",
-       "1080190                          6.93                    53.66   \n",
-       "1080191                          6.93                    53.66   \n",
-       "1080192                          6.93                    53.66   \n",
-       "1080193                          6.93                    53.66   \n",
-       "1080194                          6.93                    53.66   \n",
-       "\n",
-       "         solar_radiation_set_1  \n",
-       "555845                   147.1  \n",
-       "555846                   147.1  \n",
-       "555847                   147.1  \n",
-       "555848                   147.1  \n",
-       "555849                   147.1  \n",
-       "...                        ...  \n",
-       "1080190                  347.9  \n",
-       "1080191                  347.9  \n",
-       "1080192                  347.9  \n",
-       "1080193                  347.9  \n",
-       "1080194                  347.9  \n",
-       "\n",
-       "[524350 rows x 16 columns]"
+       "[<matplotlib.lines.Line2D at 0x1d3fea8bb10>]"
       ]
      },
-     "execution_count": 168,
+     "execution_count": 297,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_filtered"
+    "merged['date'] = pd.to_datetime(merged['date'], format = \"%Y-%m-%d %H:%M:%S\")\n",
+    "plt.plot(merged['date'],merged['wifi_fourth_south'])\n",
+    "# plt.plot(merged['date'],merged['hp_hws_temp'])\n",
+    "# plt.plot(df_filtered['date'],df_filtered['hp_hws_temp'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1d34127ba90>]"
+       "False"
       ]
      },
-     "execution_count": 170,
+     "execution_count": 234,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "plt.plot(dd[\"hp_hws_temp\"])\n",
-    "plt.plot(dg[\"hp_hws_temp\"])"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from sklearn.mixture import GaussianMixture\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Generating random data for demonstration\n",
-    "np.random.seed(0)\n",
-    "X = processed_data\n",
-    "\n",
-    "# Creating the GMM instance with desired number of clusters\n",
-    "gmm = GaussianMixture(n_components=2)\n",
-    "\n",
-    "# Fitting the model to the data\n",
-    "gmm.fit(X)\n",
-    "\n",
-    "# Getting the cluster labels\n",
-    "labels = gmm.predict(X)\n",
-    "\n",
-    "# Plotting the data points with colors representing different clusters\n",
-    "plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', alpha=0.5)\n",
-    "plt.title('GMM Clustering')\n",
-    "plt.xlabel('Feature 1')\n",
-    "plt.ylabel('Feature 2')\n",
-    "plt.show()\n"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",