Update solar_panel_control.py

src/energy_management/solar_panel_control.py

@@ -1,15 +1,143 @@
-import random
+# solar_panel_control.py
 
-class SolarPanelController:
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_squared_error, r2_score
+import matplotlib.pyplot as plt
+import seaborn as sns
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+
+class SolarPanel:
+    def __init__(self, name, capacity, current_output, angle):
+        self.name = name
+        self.capacity = capacity  # in kW
+        self.current_output = current_output  # in kW
+        self.angle = angle  # in degrees
+
+    def display_info(self):
+        """Display information about the solar panel."""
+        print(f"Solar Panel: {self.name}, Capacity: {self.capacity} kW, Current Output: {self.current_output} kW, Angle: {self.angle}°")
+
+    def adjust_angle(self, new_angle):
+        """Adjust the angle of the solar panel."""
+        self.angle = new_angle
+        print(f"Adjusted angle to {self.angle}°")
+
+class SolarPanelManager:
     def __init__(self):
-        self.efficiency = 0.18  # 18% efficiency for solar panels
-
-    def harvest_energy(self):
-        """
-        Simulates energy harvesting from solar panels.
-        
-        :return: Amount of energy harvested in watt-hours
-        """
-        sunlight_hours = random.uniform(4, 8)  # Simulate 4 to 8 hours of sunlight
-        energy_harvested = sunlight_hours * 1000 * self.efficiency  # 1000 W panel
-        return round(energy_harvested, 2)  # Return energy in Wh
+        self.panels = []
+
+    def add_panel(self, solar_panel):
+        """Add a solar panel to the manager."""
+        self.panels.append(solar_panel)
+
+    def display_panels(self):
+        """Display all solar panels."""
+        for panel in self.panels:
+            panel.display_info()
+
+    def analyze_performance(self):
+        """Analyze the performance of solar panels."""
+        total_capacity = sum(panel.capacity for panel in self.panels)
+        total_output = sum(panel.current_output for panel in self.panels)
+        avg_output = total_output / len(self.panels) if self.panels else 0
+        print(f"Total Capacity: {total_capacity} kW, Total Output: {total_output} kW, Average Output: {avg_output:.2f} kW")
+
+    def predict_output(self, historical_data):
+        """Predict solar panel output based on historical data."""
+        print("Predicting Solar Panel Output...")
+        X = historical_data[['Sunlight_Hours', 'Temperature']]
+        y = historical_data['Output']
+
+        # Split the data into training and testing sets
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+        # Train a Random Forest Regressor
+        model = RandomForestRegressor(n_estimators=100, random_state=42)
+        model.fit(X_train, y_train)
+
+        # Make predictions
+        predictions = model.predict(X_test)
+
+        # Evaluate the model
+        mse = mean_squared_error(y_test, predictions)
+        r2 = r2_score(y_test, predictions)
+        print(f"Mean Squared Error: {mse:.2f}")
+        print(f"R^2 Score: {r2:.2f}")
+
+        # Visualize predictions
+        plt.figure(figsize=(10, 5))
+        plt.scatter(X_test['Sunlight_Hours'], y_test, color='blue', label='Actual Output')
+        plt.scatter(X_test['Sunlight_Hours'], predictions, color='red', label='Predicted Output')
+        plt.xlabel('Sunlight Hours')
+        plt.ylabel('Output (kW)')
+        plt.title('Solar Panel Output Prediction')
+        plt.legend()
+        plt.show()
+
+    def optimize_output(self, sunlight_data):
+        """Optimize solar panel output based on sunlight data."""
+        print("Optimizing Solar Panel Output...")
+        # Define a neural network model
+        model = keras.Sequential([
+            layers.Dense(64, activation='relu', input_shape=(sunlight_data.shape[1],)),
+            layers.Dense(32, activation='relu'),
+            layers.Dense(1)
+        ])
+
+        # Compile the model
+        model.compile(optimizer='adam', loss='mean_squared_error')
+
+        # Train the model
+        model.fit(sunlight_data, epochs=100, verbose=0)
+
+        # Make predictions
+        optimized_output = model.predict(sunlight_data)
+
+        # Visualize optimized output
+        plt.figure(figsize=(10, 5))
+        plt.plot(sunlight_data, label='Actual Solar Output')
+        plt.plot(optimized_output, label='Optimized Solar Output')
+        plt.xlabel('Time')
+        plt.ylabel('Solar Output (kW)')
+        plt.title('Optimized Solar Panel Output')
+        plt.legend()
+        plt.show()
+
+# Example usage
+if __name__ == "__main__":
+    # Create a SolarPanelManager instance
+    solar _panel_manager = SolarPanelManager()
+
+    # Load solar panel data (name, capacity, current_output, angle)
+    solar_panel_data = [
+        ("Panel 1", 100, 80, 30),
+        ("Panel 2", 150, 120, 45),
+        ("Panel 3", 200, 180, 60)
+    ]
+
+    for data in solar_panel_data:
+        solar_panel = SolarPanel(*data)
+        solar_panel_manager.add_panel(solar_panel)
+
+    # Display solar panels
+    solar_panel_manager.display_panels()
+
+    # Analyze solar panel performance
+    solar_panel_manager.analyze_performance()
+
+    # Predict solar panel output based on historical data
+    historical_data = pd.DataFrame({
+        'Sunlight_Hours': [4, 5, 6, 7, 8],
+        'Temperature': [20, 25, 30, 35, 40],
+        'Output': [80, 90, 100, 110, 120]
+    })
+    solar_panel_manager.predict_output(historical_data)
+
+    # Optimize solar panel output based on sunlight data
+    sunlight_data = np.array([4, 5, 6, 7, 8])
+    solar_panel_manager.optimize_output(sunlight_data)