add seperate RTU anomalizer

src/main.py

@@ -1,5 +1,6 @@
 import json
-from rtu.RTUAnomalizer import RTUAnomalizer
+from rtu.RTUAnomalizer1 import RTUAnomalizer1
+from rtu.RTUAnomalizer2 import RTUAnomalizer2
 from rtu.RTUPipeline import RTUPipeline
 from vav.VAVPipeline import VAVPipeline
 from vav.VAVAnomalizer import VAVAnomalizer
@@ -7,32 +8,42 @@ import paho.mqtt.client as mqtt
 
 
 def main():
-    rtu_data_pipeline = RTUPipeline(scaler_path="src/rtu/models/scaler_1.pkl")
-    rtu_anomalizer = RTUAnomalizer(
-        prediction_model_path="src/rtu/models/lstm_4rtu_smooth_02.keras",
+    rtu_data_pipeline = RTUPipeline(scaler1_path="src/rtu/models/scaler_rtu_1_2.pkl",scaler2_path="src/rtu/models/scaler_rtu_3_4.pkl")
+    #RTU - 1, 2
+    rtu_anomalizer1 = RTUAnomalizer1(
+        prediction_model_path="src/rtu/models/lstm_2rtu_smooth_04.keras",
         clustering_model_paths=[
-            "src/rtu/models/kmeans_model1.pkl",
-            "src/rtu/models/kmeans_model2.pkl",
-            "src/rtu/models/kmeans_model3.pkl",
-            "src/rtu/models/kmeans_model4.pkl",
+            "src/rtu/models/kmeans_rtu_1.pkl",
+            "src/rtu/models/kmeans_rtu_2.pkl",
+        ],
+        num_inputs=rtu_data_pipeline.num_inputs,
+        num_outputs=rtu_data_pipeline.num_outputs,
+    )
+    #RTU - 3,4
+    rtu_anomalizer2 = RTUAnomalizer2(
+        prediction_model_path="src/rtu/models/lstm_2rtu_smooth_03.keras",
+        clustering_model_paths=[
+            "src/rtu/models/kmeans_rtu_3.pkl",
+            "src/rtu/models/kmeans_rtu_4.pkl",
         ],
         num_inputs=rtu_data_pipeline.num_inputs,
         num_outputs=rtu_data_pipeline.num_outputs,
     )
 
-    vav_pipeline = VAVPipeline(rtu_id=1, scaler_path="src/vav/models/scaler_vav_1.pkl")
+    # vav_pipeline = VAVPipeline(rtu_id=1, scaler_path="src/vav/models/scaler_vav_1.pkl")
 
-    vav_anomalizer = VAVAnomalizer(prediction_model_path="src/vav/models/lstm__vav_01")
+    # vav_anomalizer = VAVAnomalizer(prediction_model_path="src/vav/models/lstm__vav_01")
     # print(vav_pipeline.input_col_names)
 
     # print(len(vav_pipeline.output_col_names))
 
     def on_message(client, userdata, message):
         # print(json.loads(message.payload.decode()))
-        df_new, df_trans = rtu_data_pipeline.fit(message)
-        if not df_new is None and not df_trans is None:
-            out1,out2,out3,out4 = rtu_anomalizer.pipeline(df_new, df_trans, rtu_data_pipeline.scaler)
-            print(out3)
+        df_new1, df_trans1, df_new2, df_trans2 = rtu_data_pipeline.fit(message)    
+        if not df_new1 is None and not df_trans1 is None and not df_new2 is None and not df_trans2 is None:
+            out1,out2,out3,out4 = rtu_anomalizer1.pipeline(df_new1, df_trans1, rtu_data_pipeline.scaler1)
+            out5,out6,out7,out8 = rtu_anomalizer2.pipeline(df_new2, df_trans2, rtu_data_pipeline.scaler2)
+            print(out2)
 
     broker_address = "localhost"
     broker_port = 1883

src/rtu/{RTUAnomalizer.py → RTUAnomalizer1.py}

@@ -3,7 +3,7 @@ from tensorflow.keras.models import load_model
 import joblib
 
 
-class RTUAnomalizer:
+class RTUAnomalizer1:
     """
     Class for performing anomaly detection on RTU (Roof Top Unit) data.
     """
@@ -31,6 +31,7 @@ class RTUAnomalizer:
         self.num_outputs = num_outputs
         if prediction_model_path is not None and clustering_model_paths is not None:
             self.load_models(prediction_model_path, clustering_model_paths)
+        self.actual_list, self.pred_list, self.resid_list = self.initialize_lists()
 
     def initialize_lists(self, size=30):
         """
@@ -42,7 +43,7 @@ class RTUAnomalizer:
         Returns:
             tuple: A tuple containing three lists initialized with zeros.
         """
-        initial_values = [0] * size
+        initial_values = [[0]*self.num_outputs] * size
         return initial_values.copy(), initial_values.copy(), initial_values.copy()
 
     def load_models(self, prediction_model_path, clustering_model_paths):
@@ -68,7 +69,7 @@ class RTUAnomalizer:
         Returns:
             array: Predicted values.
         """
-        return self.model.predict(df_new)
+        return self.model.predict(df_new,verbose=0)
 
     def calculate_residuals(self, df_trans, pred):
         """
@@ -120,7 +121,7 @@ class RTUAnomalizer:
         actual = scaler.inverse_transform(np.array([df_trans[30, :]]))
         return actual, pred
 
-    def update_lists(self, actual_list, pred_list, resid_list, actual, pred, resid):
+    def update_lists(self, actual, pred, resid):
         """
         Update the lists of actual, predicted, and residual values.
 
@@ -135,13 +136,13 @@ class RTUAnomalizer:
         Returns:
             tuple: A tuple containing the updated lists of actual, predicted, and residual values.
         """
-        actual_list.pop(0)
-        pred_list.pop(0)
-        resid_list.pop(0)
-        actual_list.append(actual[0, 1])
-        pred_list.append(pred[0, 1])
-        resid_list.append(resid[0, 1])
-        return actual_list, pred_list, resid_list
+        self.actual_list.pop(0)
+        self.pred_list.pop(0)
+        self.resid_list.pop(0)
+        self.actual_list.append(actual.flatten().tolist())
+        self.pred_list.append(pred.flatten().tolist())
+        self.resid_list.append(resid.flatten().tolist())
+        return self.actual_list, self.pred_list, self.resid_list
 
     def calculate_distances(self, resid):
         """
@@ -177,13 +178,12 @@ class RTUAnomalizer:
         Returns:
             tuple: A tuple containing the lists of actual, predicted, and residual values, and the distances.
         """
-        actual_list, pred_list, resid_list = self.initialize_lists()
+        
         pred = self.predict(df_new)
         actual, resid = self.calculate_residuals(df_trans, pred)
         pred = self.resize_prediction(pred, df_trans)
         actual, pred = self.inverse_transform(scaler, pred, df_trans)
         actual_list, pred_list, resid_list = self.update_lists(
-            actual_list, pred_list, resid_list, actual, pred, resid
-        )
+             actual, pred, resid)
         dist = self.calculate_distances(resid)
         return actual_list, pred_list, resid_list, dist

src/rtu/RTUAnomalizer2.py

@@ -0,0 +1,189 @@
+import numpy as np
+from tensorflow.keras.models import load_model
+import joblib
+
+
+class RTUAnomalizer2:
+    """
+    Class for performing anomaly detection on RTU (Roof Top Unit) data.
+    """
+
+    model = None
+    kmeans_models = []
+
+    def __init__(
+        self,
+        prediction_model_path=None,
+        clustering_model_paths=None,
+        num_inputs=None,
+        num_outputs=None,
+    ):
+        """
+        Initialize the RTUAnomalizer object.
+
+        Args:
+            prediction_model_path (str): Path to the prediction model file.
+            clustering_model_paths (list): List of paths to the clustering model files.
+            num_inputs (int): Number of input features.
+            num_outputs (int): Number of output features.
+        """
+        self.num_inputs = num_inputs
+        self.num_outputs = num_outputs
+        if prediction_model_path is not None and clustering_model_paths is not None:
+            self.load_models(prediction_model_path, clustering_model_paths)
+        self.actual_list, self.pred_list, self.resid_list = self.initialize_lists()
+
+    def initialize_lists(self, size=30):
+        """
+        Initialize lists for storing actual, predicted, and residual values.
+
+        Args:
+            size (int): Size of the lists.
+
+        Returns:
+            tuple: A tuple containing three lists initialized with zeros.
+        """
+        initial_values = [[0]*self.num_outputs] * size
+        return initial_values.copy(), initial_values.copy(), initial_values.copy()
+
+    def load_models(self, prediction_model_path, clustering_model_paths):
+        """
+        Load the prediction and clustering models.
+
+        Args:
+            prediction_model_path (str): Path to the prediction model file.
+            clustering_model_paths (list): List of paths to the clustering model files.
+        """
+        self.model = load_model(prediction_model_path)
+
+        for path in clustering_model_paths:
+            self.kmeans_models.append(joblib.load(path))
+
+    def predict(self, df_new):
+        """
+        Make predictions using the prediction model.
+
+        Args:
+            df_new (DataFrame): Input data for prediction.
+
+        Returns:
+            array: Predicted values.
+        """
+        return self.model.predict(df_new,verbose=0)
+
+    def calculate_residuals(self, df_trans, pred):
+        """
+        Calculate the residuals between actual and predicted values.
+
+        Args:
+            df_trans (DataFrame): Transformed input data.
+            pred (array): Predicted values.
+
+        Returns:
+            tuple: A tuple containing the actual values and residuals.
+        """
+        actual = df_trans[30, : self.num_outputs]
+        resid = actual - pred
+        return actual, resid
+
+    def resize_prediction(self, pred, df_trans):
+        """
+        Resize the predicted values to match the shape of the transformed input data.
+
+        Args:
+            pred (array): Predicted values.
+            df_trans (DataFrame): Transformed input data.
+
+        Returns:
+            array: Resized predicted values.
+        """
+        pred = np.resize(
+            pred, (pred.shape[0], pred.shape[1] + len(df_trans[30, self.num_outputs :]))
+        )
+        pred[:, -len(df_trans[30, self.num_outputs :]) :] = df_trans[
+            30, self.num_outputs :
+        ]
+        return pred
+
+    def inverse_transform(self, scaler, pred, df_trans):
+        """
+        Inverse transform the predicted and actual values.
+
+        Args:
+            scaler (object): Scaler object for inverse transformation.
+            pred (array): Predicted values.
+            df_trans (DataFrame): Transformed input data.
+
+        Returns:
+            tuple: A tuple containing the actual and predicted values after inverse transformation.
+        """
+        pred = scaler.inverse_transform(np.array(pred))
+        actual = scaler.inverse_transform(np.array([df_trans[30, :]]))
+        return actual, pred
+
+    def update_lists(self, actual, pred, resid):
+        """
+        Update the lists of actual, predicted, and residual values.
+
+        Args:
+            actual_list (list): List of actual values.
+            pred_list (list): List of predicted values.
+            resid_list (list): List of residual values.
+            actual (array): Actual values.
+            pred (array): Predicted values.
+            resid (array): Residual values.
+
+        Returns:
+            tuple: A tuple containing the updated lists of actual, predicted, and residual values.
+        """
+        self.actual_list.pop(0)
+        self.pred_list.pop(0)
+        self.resid_list.pop(0)
+        self.actual_list.append(actual.flatten().tolist())
+        self.pred_list.append(pred.flatten().tolist())
+        self.resid_list.append(resid.flatten().tolist())
+        return self.actual_list, self.pred_list, self.resid_list
+
+    def calculate_distances(self, resid):
+        """
+        Calculate the distances between residuals and cluster centers.
+
+        Args:
+            resid (array): Residual values.
+
+        Returns:
+            array: Array of distances.
+        """
+        dist = []
+        for i, model in enumerate(self.kmeans_models):
+            dist.append(
+                np.linalg.norm(
+                    resid[:, (i * 7) + 1 : (i * 7) + 8] - model.cluster_centers_[0],
+                    ord=2,
+                    axis=1,
+                )
+            )
+
+        return np.array(dist)
+
+    def pipeline(self, df_new, df_trans, scaler):
+        """
+        Perform the anomaly detection pipeline.
+
+        Args:
+            df_new (DataFrame): Input data for prediction.
+            df_trans (DataFrame): Transformed input data.
+            scaler (object): Scaler object for inverse transformation.
+
+        Returns:
+            tuple: A tuple containing the lists of actual, predicted, and residual values, and the distances.
+        """
+        
+        pred = self.predict(df_new)
+        actual, resid = self.calculate_residuals(df_trans, pred)
+        pred = self.resize_prediction(pred, df_trans)
+        actual, pred = self.inverse_transform(scaler, pred, df_trans)
+        actual_list, pred_list, resid_list = self.update_lists(
+             actual, pred, resid)
+        dist = self.calculate_distances(resid)
+        return actual_list, pred_list, resid_list, dist

src/rtu/RTUPipeline.py

@@ -7,9 +7,10 @@ import numpy as np
 
 
 class RTUPipeline:
-    scaler = None
+    scaler1 = None # RTU 1,2
+    scaler2 = None # RTU 3,4
 
-    def __init__(self, rtus=[1, 2], scaler_path=None):
+    def __init__(self, rtus=[1, 2, 3, 4], scaler1_path=None,scaler2_path=None):
 
         outputs = [
             "sa_temp",
@@ -27,20 +28,28 @@ class RTUPipeline:
         for rtu in rtus:
             for output in outputs:
                 self.output_col_names.append(f"rtu_00{rtu}_{output}")
+                
+        self.input_col_names = []
+                
+        for rtu in rtus:
+            self.input_col_names.append(f"rtu_00{rtu}_sat_sp_tn")        
 
-        self.input_col_names = [
+        self.input_col_names = self.input_col_names + [
             "air_temp_set_1",
             "air_temp_set_2",
             "dew_point_temperature_set_1d",
             "relative_humidity_set_1",
             "solar_radiation_set_1",
         ]
-        self.num_inputs = len(self.input_col_names)
-        self.num_outputs = len(self.output_col_names)
+        self.num_inputs = len(self.input_col_names)-2
+        self.num_outputs = len(self.output_col_names)-14
         self.column_names = self.output_col_names + self.input_col_names
 
-        if scaler_path:
-            self.scaler = self.get_scaler(scaler_path)
+        if scaler1_path:
+            self.scaler1 = self.get_scaler(scaler1_path)
+        if scaler2_path:
+            self.scaler2 = self.get_scaler(scaler2_path)
+            
         self.df = pd.DataFrame(columns=self.column_names)
 
     def get_scaler(self, scaler_path):
@@ -48,16 +57,20 @@ class RTUPipeline:
 
     def get_window(self, df):
         len_df = len(df)
+        print(len_df)
         if len_df > 30:
+            df = df.rolling(window=30,min_periods=1).mean()
             return df[len_df - 31 : len_df].astype("float32")
         else:
             return None
 
     def transform_window(self, df_window):
-        return self.scaler.transform(df_window)
+        columns_scaler1 = [0] + list(range(1,15)) + [29,30] + list(range(33, 38))
+        columns_scaler2 = [0] + list(range(15, 29)) + [31,32] + list(range(33, 38))
+        return self.scaler1.transform(df_window.iloc[:, columns_scaler1]),self.scaler2.transform(df_window.iloc[:, columns_scaler2])
 
     def prepare_input(self, df_trans):
-        return df_trans[:30, :].reshape((1, 30, len(self.column_names)))
+        return df_trans[:30, :].reshape((1, 30, len(self.column_names)-16))
 
     def extract_data_from_message(self, message):
         payload = json.loads(message.payload.decode())
@@ -74,9 +87,12 @@ class RTUPipeline:
         df = self.extract_data_from_message(message)
         df_window = self.get_window(df)
         if df_window is not None:
-            df_trans = self.transform_window(df_window)
-            df_new = self.prepare_input(df_trans)
+            df_trans1,df_trans2 = self.transform_window(df_window)
+            df_new1 = self.prepare_input(df_trans1)
+            df_new2 = self.prepare_input(df_trans2)
         else:
-            df_new = None
-            df_trans = None
-        return df_new, df_trans
+            df_new1 = None
+            df_trans1 = None
+            df_new2 = None
+            df_trans2 = None
+        return df_new1, df_trans1, df_new2, df_trans2

src/rtu/models/kmeans_rtu_1.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:42621b20e5f4048526e82615e92de0031ba42c540fbaba637efe0b4506f13ff4
+size 2065925

src/rtu/models/kmeans_rtu_2.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e1c07513633ff899c5b1fa46bdf750420ee95593282f79bc4564db06c2dfb601
+size 2065925

src/rtu/models/kmeans_rtu_3.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:31d6d81362dde61976f0617e51239a67c6962c1ea84f6c301970bafdb8406146
+size 2065881

src/rtu/models/kmeans_rtu_4.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:72a5a1799b4546825815d9833598bcce7fb3241f9c15a3c03c3d0c28e5010b33
+size 2065881

lstm.ipynb → src/rtu/models/lstm.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -434,7 +434,7 @@
        "[2072154 rows x 30 columns]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1135,7 +1135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -1272,7 +1272,7 @@
        "[2 rows x 65 columns]"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1294,7 +1294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -1335,7 +1335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1354,20 +1354,20 @@
     "#  '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",
-    "#  '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",
-    "#  '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_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",
+    " '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",
@@ -1375,6 +1375,7 @@
     " 'rtu_001_ma_temp',\n",
     " 'rtu_001_sf_vfd_spd_fbk_tn',\n",
     " 'rtu_001_rf_vfd_spd_fbk_tn',\n",
+    " \n",
     " 'rtu_002_sa_temp',\n",
     " 'rtu_002_oadmpr_pct',\n",
     " 'rtu_002_ra_temp',\n",
@@ -1382,8 +1383,9 @@
     " 'rtu_002_ma_temp',\n",
     " 'rtu_002_sf_vfd_spd_fbk_tn',\n",
     " 'rtu_002_rf_vfd_spd_fbk_tn',\n",
-    "#  'rtu_003_sat_sp_tn',\n",
-    "#  'rtu_004_sat_sp_tn',\n",
+    " \n",
+    " 'rtu_003_sat_sp_tn',\n",
+    " 'rtu_004_sat_sp_tn',\n",
     " 'rtu_001_sat_sp_tn',\n",
     " 'rtu_002_sat_sp_tn',\n",
     " 'air_temp_set_1',\n",
@@ -1395,7 +1397,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_filtered = df_filtered.dropna()\n",
+    "df_filtered.to_csv(\"sample_test_data.csv\",index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1435,7 +1447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1459,64 +1471,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 250,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['scaler_1.pkl']"
+       "['scaler_rtu_3_4.pkl']"
       ]
      },
-     "execution_count": 250,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "import joblib\n",
-    "# joblib.dump(scaler, 'scaler_1.pkl')\n",
+    "joblib.dump(scaler, 'scaler_rtu_3_4.pkl')\n",
     "# loaded_scaler = joblib.load('scaler.pkl')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 23,
    "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 1/2\n",
-      "\u001b[1m8066/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0548\n",
-      "Epoch 1: val_loss improved from inf to 0.34488, saving model to lstm_2rtu_smooth_04.keras\n",
-      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m165s\u001b[0m 20ms/step - loss: 0.0548 - val_loss: 0.3449\n",
-      "Epoch 2/2\n",
-      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0014\n",
-      "Epoch 2: val_loss improved from 0.34488 to 0.27523, saving model to lstm_2rtu_smooth_04.keras\n",
-      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m160s\u001b[0m 20ms/step - loss: 0.0014 - val_loss: 0.2752\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<keras.src.callbacks.history.History at 0x207bef58850>"
-      ]
-     },
-     "execution_count": 115,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#2 rtu model\n",
     "\n",
@@ -1543,17 +1522,17 @@
     "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",
+    "# 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",
+    "# model.compile(optimizer='adam', loss='mean_squared_error')\n",
     "\n",
-    "checkpoint_path = \"lstm_2rtu_smooth_04.keras\" #\"lstm_2rtu_smooth_03.keras\"--> 3,4rtu\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=2, batch_size=64, verbose=1, callbacks=[checkpoint_callback])"
+    "# checkpoint_path = \"lstm_2rtu_smooth_04.keras\" #\"lstm_2rtu_smooth_03.keras\"--> 3,4rtu\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=2, batch_size=64, verbose=1, callbacks=[checkpoint_callback])"
    ]
   },
   {
@@ -1697,7 +1676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1715,15 +1694,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m19190/19190\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 4ms/step\n",
-      "\u001b[1m16134/16134\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 4ms/step\n"
+      "\u001b[1m19190/19190\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 4ms/step\n",
+      "\u001b[1m16134/16134\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 4ms/step\n"
      ]
     }
    ],
@@ -1809,16 +1788,425 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 0.00056891 -0.00128509  0.00987136 -0.00519864  0.01173089  0.00975552\n",
-      "   0.01170705]]\n"
-     ]
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\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-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 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-2 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-2 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-2 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-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 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-2 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-2 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-2 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-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 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-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2\" 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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" 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": 26,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -1846,44 +2234,45 @@
     "\n",
     "\n",
     "\n",
-    "pca = PCA(n_components=2)\n",
-    "X = pca.fit_transform(X1)\n",
+    "# pca = PCA(n_components=2)\n",
+    "# X = pca.fit_transform(X1)\n",
     "\n",
     "\n",
     "\n",
     "\n",
-    "# # Getting the cluster centers and labels\n",
-    "centroids = pca.transform(kmeans1.cluster_centers_)\n",
-    "labels = kmeans1.labels_\n",
-    "print(kmeans1.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"
+    "# # # Getting the cluster centers and labels\n",
+    "# centroids = pca.transform(kmeans1.cluster_centers_)\n",
+    "# labels = kmeans1.labels_\n",
+    "# print(kmeans1.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,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['kmeans_model4.pkl']"
+       "['kmeans_rtu_4.pkl']"
       ]
      },
-     "execution_count": 249,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "import joblib\n",
-    "# joblib.dump(kmeans4, 'kmeans_model4.pkl')\n",
+    "joblib.dump(kmeans1, 'kmeans_rtu_3.pkl')\n",
+    "joblib.dump(kmeans2, 'kmeans_rtu_4.pkl')\n",
     "# joblib.dump(pca, 'pca_model.pkl')\n"
    ]
   },
@@ -1915,7 +2304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2169,38 +2558,6 @@
     "# plt.plot(df_filtered['date'],df_filtered['hp_hws_temp'])"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 234,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 234,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,

src/rtu/models/scaler_rtu_1_2.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0ba51191b0f354f482615235908b9f3934b0986b8b97b3c34ef942c8d4706b5d
+size 1149

src/rtu/models/scaler_rtu_3_4.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:096b8e146b1d09dac40661624f33d902e44eb19c8e2ca33b8854022ce2f77796
+size 1149