start on adding energy prediction to main.py

.gitignore

@@ -4,4 +4,5 @@ venv
 __pycache__/
 *.tf
 data
-*.csv
+*.csv
+src/test_main.py

src/energy_prediction/EnergyPredictionNorth.py

@@ -0,0 +1,59 @@
+import numpy as np
+import pandas as pd
+from tensorflow.keras.models import load_model
+
+class EnergyPredictionNorth:
+    """
+    Class for predicting energy consumption in the north wing of the building.
+    """
+
+    def __init__(self, 
+                 model_path=None):
+        """
+        Initialize the EnergyPredictionNorth object.
+
+        Args:
+            model_path (str): Path to the prediction model file.
+        """
+        if model_path is not None:
+            self.load_model(model_path)
+
+    def load_model(self, model_path):
+        """
+        Load the prediction model.
+
+        Args:
+            model_path (str): Path to the prediction model file.
+        """
+        self.model = load_model(model_path)
+
+    def predict(self, data):
+        """
+        Predict energy consumption based on the input data.
+
+        Args:
+            data (pd.DataFrame): Input data for prediction.
+
+        Returns:
+            np.ndarray: Predicted energy consumption values.
+        """
+        return self.model.predict(data, verbose=0)
+    
+    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.
+        """
+        mean = scaler.mean_[0]
+        std = scaler.scale_[0]
+
+        pred    = pred * std + mean    
+        actual  = df_trans[:,0] * std + mean
+        return actual, pred

src/energy_prediction/EnergyPredictionPipeline.py

@@ -0,0 +1,15 @@
+import pandas as pd
+
+class EnergyPredictionPipeline:
+    def __init__(self, model_path_north, model_path_south):
+        self.model_north = EnergyPredictionNorth(model_path_north)
+        self.model_south = EnergyPredictionSouth(model_path_south)
+
+    def predict(self, data):
+        north_data = data[data["wing"] == "north"]
+        south_data = data[data["wing"] == "south"]
+
+        north_prediction = self.model_north.predict(north_data)
+        south_prediction = self.model_south.predict(south_data)
+
+        return pd.concat([north_prediction, south_prediction])

src/main.py

@@ -4,6 +4,7 @@ from rtu.RTUAnomalizer2 import RTUAnomalizer2
 from rtu.RTUPipeline import RTUPipeline
 from vav.VAVPipeline import VAVPipeline
 from vav.VAVAnomalizer import VAVAnomalizer
+from energy_prediction.EnergyPredictionNorth import EnergyPredictionNorth
 import paho.mqtt.client as mqtt
 
 
@@ -43,6 +44,9 @@ def main():
 
     # print(len(vav_pipeline.output_col_names))
 
+
+
+
     def on_message(client, userdata, message):
         df_new_vav, df_trans_vav = vav_pipeline.fit(message)
         vav_anomalizer.num_inputs = vav_pipeline.num_inputs