Initial commit: Energy consumption prediction model

.gitattributes

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+*.joblib filter=lfs diff=lfs merge=lfs -text

README.md

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+# Energy Consumption Prediction Model
+
+A Random Forest model for predicting household energy consumption patterns and costs.
+
+## Model Description
+
+This model predicts monthly energy consumption in kWh and associated costs in PLN (Polish Złoty) based on historical consumption patterns and seasonal features.
+
+**Model Type:** Random Forest Regressor  
+**Framework:** scikit-learn  
+**Performance:** R² = 0.848  
+
+## Features
+
+The model uses 17 engineered features including:
+- **Moving averages** (3-month and 6-month windows)
+- **Lag features** (1, 2, 3 months back)
+- **Seasonal indicators** (winter, summer, transition periods)
+- **Temporal features** (month, year, day of year, quarter)
+- **Cyclical encoding** (sin/cos transforms for monthly patterns)
+
+## Usage
+
+```python
+import sys
+sys.path.append('.')  # Add current directory to path
+
+from model import EnergyConsumptionPredictor
+
+# Load the pre-trained model
+model = EnergyConsumptionPredictor.from_file('energy_model_latest.joblib')
+
+# Make predictions for next 6 months
+predictions = model.predict_future(months=6)
+
+# Display results
+print(predictions[['Date', 'Predicted_Consumption', 'Predicted_Cost']])
+```
+
+## Output Format
+
+The model returns a pandas DataFrame with columns:
+- `Date`: Month start date
+- `Predicted_Consumption`: Predicted consumption in kWh
+- `Predicted_Cost`: Predicted cost in PLN
+- `Month`: Month number (1-12)
+- `Year`: Year
+
+## Requirements
+
+```
+pandas>=2.0.0
+scikit-learn>=1.3.0
+numpy>=1.24.0
+joblib>=1.3.0
+```
+
+## Model Training Data
+
+The model was trained on residential energy consumption data with:
+- **17 data points** spanning multiple months
+- Features include seasonal patterns, consumption history, and temporal indicators
+- Target variable: Monthly energy consumption in kWh
+
+## Performance Metrics
+
+- **R² Score:** 0.848
+- **Model Type:** Random Forest (100 estimators)
+- **Cross-validation:** 3-fold CV used for model selection
+
+## Feature Importance
+
+Top 5 most important features:
+1. `consumption_ma_3` (3-month moving average)
+2. `consumption_ma_6` (6-month moving average) 
+3. `consumption_lag_1` (1-month lag)
+4. `consumption_lag_3` (3-month lag)
+5. `month_sin` (seasonal encoding)
+
+## Cost Calculation
+
+The model calculates costs using Polish energy pricing structure:
+- Energy rate per kWh
+- Distribution fees
+- VAT (Value Added Tax)
+
+## Limitations
+
+- Model is trained on Polish residential data
+- Cost calculations use Polish energy pricing
+- Designed for monthly predictions
+- Performance may vary for different consumption patterns
+
+## Example Output
+
+```
+        Date  Predicted_Consumption  Predicted_Cost
+0 2025-06-01                    191              216
+1 2025-07-01                    135              153
+2 2025-08-01                    199              224
+```
+
+## License
+
+This model is provided as-is for demonstration and educational purposes.

energy_model_latest.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e2096ae0d78afc040c226d02ddc89be94ac7cc5212b1afd753c228c29eb9adf2
+size 307968

example.py

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+"""
+Example usage of Energy Consumption Prediction Model
+Download this file along with model.py and energy_model_latest.joblib
+"""
+
+import pandas as pd
+import numpy as np
+from datetime import datetime
+import os
+
+def main():
+    # Check if model file exists
+    model_path = 'energy_model_latest.joblib'
+    if not os.path.exists(model_path):
+        print(f"Error: {model_path} not found!")
+        print("Please download energy_model_latest.joblib from this repository")
+        return
+    
+    # Import and load model
+    from model import EnergyConsumptionPredictor
+    
+    print("Loading energy consumption prediction model...")
+    model = EnergyConsumptionPredictor.from_file(model_path)
+    
+    print(f"Model loaded successfully: {model.best_model_name}")
+    print(f"Features used: {len(model.feature_columns)}")
+    
+    # Make predictions
+    months_to_predict = 6
+    print(f"\nPredicting energy consumption for next {months_to_predict} months...")
+    
+    predictions = model.predict_future(months=months_to_predict)
+    
+    # Display results
+    print("\n" + "="*60)
+    print("ENERGY CONSUMPTION PREDICTIONS")
+    print("="*60)
+    
+    total_consumption = predictions['Predicted_Consumption'].sum()
+    total_cost = predictions['Predicted_Cost'].sum()
+    avg_consumption = total_consumption / months_to_predict
+    avg_cost = total_cost / months_to_predict
+    
+    print(f"Total predicted consumption: {total_consumption:.0f} kWh")
+    print(f"Total predicted cost: {total_cost:.0f} PLN")
+    print(f"Average monthly consumption: {avg_consumption:.0f} kWh")
+    print(f"Average monthly cost: {avg_cost:.0f} PLN")
+    
+    print(f"\nMonthly breakdown:")
+    print("-" * 55)
+    print(f"{'Month':<15} {'Consumption':<15} {'Cost (PLN)'}")
+    print("-" * 55)
+    
+    for _, row in predictions.iterrows():
+        month_name = row['Date'].strftime('%B %Y')
+        consumption = row['Predicted_Consumption']
+        cost = row['Predicted_Cost']
+        print(f"{month_name:<15} {consumption:>8.0f} kWh {cost:>12.0f}")
+    
+    print("-" * 55)
+    
+    # Show feature importance
+    importance = model.get_feature_importance()
+    if importance:
+        print(f"\nTop 5 most important prediction features:")
+        for i, (feature, score) in enumerate(list(importance.items())[:5], 1):
+            print(f"  {i}. {feature}: {score:.3f}")
+    
+    # Save predictions to CSV
+    output_file = 'energy_predictions.csv'
+    predictions.to_csv(output_file, index=False)
+    print(f"\nPredictions saved to: {output_file}")
+    
+    return predictions
+
+if __name__ == "__main__":
+    print("Energy Consumption Prediction Model - Example Usage")
+    print("=" * 55)
+    print("Required files: model.py, energy_model_latest.joblib")
+    print("=" * 55)
+    
+    try:
+        predictions = main()
+        print(f"\n✓ Success! Generated {len(predictions)} monthly predictions")
+    except Exception as e:
+        print(f"\n✗ Error: {str(e)}")
+        print("\nMake sure you have:")
+        print("1. model.py")
+        print("2. energy_model_latest.joblib") 
+        print("3. Required packages: pandas, numpy, scikit-learn, joblib")

model.py

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+import pandas as pd
+import numpy as np
+from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
+from sklearn.model_selection import train_test_split, cross_val_score
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
+from sklearn.linear_model import LinearRegression
+import pickle
+import joblib
+import os
+from datetime import datetime
+import warnings
+warnings.filterwarnings('ignore')
+
+class EnergyConsumptionPredictor:
+    def __init__(self):
+        self.models = {
+            'random_forest': RandomForestRegressor(n_estimators=100, random_state=42),
+            'gradient_boosting': GradientBoostingRegressor(n_estimators=100, random_state=42),
+            'linear_regression': LinearRegression()
+        }
+
+        self.best_model = None
+        self.best_model_name = None
+        self.scaler = StandardScaler()
+        self.feature_columns = None
+        self.data_stats = {}
+
+    def _create_features(self, df):
+        features_df = df.copy()
+
+        # Moving averages
+        for window in [3, 6]:
+            if len(df) > window:
+                features_df[f'consumption_ma_{window}'] = features_df['Consumption'].rolling(window=window).mean()
+                features_df[f'consumption_std_{window}'] = features_df['Consumption'].rolling(window=window).std()
+
+        # Lag features
+        for lag in [1, 2, 3]:
+            if len(df) > lag:
+                features_df[f'consumption_lag_{lag}'] = features_df['Consumption'].shift(lag)
+
+        # Seasonal indicators
+        features_df['is_winter'] = features_df['Month'].isin([12, 1, 2]).astype(int)
+        features_df['is_summer'] = features_df['Month'].isin([6, 7, 8]).astype(int)
+        features_df['is_transition'] = features_df['Month'].isin([3, 4, 5, 9, 10, 11]).astype(int)
+
+        return features_df
+
+    def _prepare_training_data(self, df):
+        features_df = self._create_features(df)
+        features_df = features_df.dropna()
+
+        exclude_columns = ['Date', 'Consumption', 'Reading', 'Cost']
+        feature_columns = [col for col in features_df.columns if col not in exclude_columns]
+        self.feature_columns = feature_columns
+
+        X = features_df[feature_columns].values
+        y = features_df['Consumption'].values
+
+        return X, y
+
+    def train(self, df):
+        # Store data statistics for predictions
+        self.data_stats = {
+            'mean_consumption': df['Consumption'].mean(),
+            'std_consumption': df['Consumption'].std(),
+            'min_date': df['Date'].min(),
+            'max_date': df['Date'].max(),
+            'seasonal_patterns': df.groupby('Month')['Consumption'].mean().to_dict()
+        }
+
+        X, y = self._prepare_training_data(df)
+
+        if len(X) < 5:
+            return self._train_baseline_model(df)
+
+        X_scaled = self.scaler.fit_transform(X)
+        X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42, shuffle=False)
+
+        model_scores = {}
+
+        for model_name, model in self.models.items():
+            model.fit(X_train, y_train)
+            y_pred = model.predict(X_test)
+
+            r2 = r2_score(y_test, y_pred)
+            rmse = np.sqrt(mean_squared_error(y_test, y_pred))
+            mae = mean_absolute_error(y_test, y_pred)
+            cv_scores = cross_val_score(model, X_scaled, y, cv=3, scoring='r2')
+
+            model_scores[model_name] = {
+                'r2_score': r2,
+                'rmse': rmse,
+                'mae': mae,
+                'cv_score': cv_scores.mean()
+            }
+
+        # Select best model based on cross-validation
+        self.best_model_name = max(model_scores.keys(), key=lambda k: model_scores[k]['cv_score'])
+        self.best_model = self.models[self.best_model_name]
+        self.best_model.fit(X_scaled, y)
+
+        final_predictions = self.best_model.predict(X_scaled)
+        return {
+            'r2_score': r2_score(y, final_predictions),
+            'rmse': np.sqrt(mean_squared_error(y, final_predictions)),
+            'mae': mean_absolute_error(y, final_predictions),
+            'model_name': self.best_model_name,
+            'all_models': model_scores
+        }
+
+    def _train_baseline_model(self, df):
+        monthly_avg = df.groupby('Month')['Consumption'].mean()
+        overall_mean = df['Consumption'].mean()
+        self.baseline_predictions = monthly_avg.fillna(overall_mean).to_dict()
+        self.best_model_name = "baseline_seasonal"
+
+        return {
+            'r2_score': 0.0,
+            'rmse': df['Consumption'].std(),
+            'mae': df['Consumption'].std() * 0.8,
+            'model_name': 'baseline_seasonal'
+        }
+
+    def predict_future(self, months=12):
+        if self.best_model_name == "baseline_seasonal":
+            return self._predict_baseline(months)
+
+        last_date = self.data_stats['max_date']
+        future_dates = pd.date_range(start=last_date + pd.DateOffset(months=1), periods=months, freq='MS')
+
+        predictions = []
+
+        for date in future_dates:
+            features = {
+                'Month': date.month,
+                'Year': date.year,
+                'DayOfYear': date.timetuple().tm_yday,
+                'Quarter': date.quarter,
+                'days_since_start': (date - self.data_stats['min_date']).days,
+                'month_sin': np.sin(2 * np.pi * date.month / 12),
+                'month_cos': np.cos(2 * np.pi * date.month / 12),
+                'is_winter': int(date.month in [12, 1, 2]),
+                'is_summer': int(date.month in [6, 7, 8]),
+                'is_transition': int(date.month in [3, 4, 5, 9, 10, 11])
+            }
+
+            # Use seasonal patterns for lag/moving average features
+            seasonal_consumption = self.data_stats['seasonal_patterns'].get(date.month, self.data_stats['mean_consumption'])
+
+            for window in [3, 6]:
+                features[f'consumption_ma_{window}'] = seasonal_consumption
+                features[f'consumption_std_{window}'] = self.data_stats['std_consumption']
+
+            for lag in [1, 2, 3]:
+                features[f'consumption_lag_{lag}'] = seasonal_consumption
+
+            feature_vector = np.array([[features[col] for col in self.feature_columns]])
+            feature_vector_scaled = self.scaler.transform(feature_vector)
+
+            prediction = self.best_model.predict(feature_vector_scaled)[0]
+            # Add some noise to make predictions more realistic
+            prediction = max(0, prediction + np.random.normal(0, self.data_stats['std_consumption'] * 0.1))
+
+            predictions.append(prediction)
+
+        # Calculate costs - using hardcoded values for standalone model
+        ENERGY_RATE = 0.6972
+        DISTRIBUTION_MULTIPLIER = 0.5068  
+        VAT_RATE = 0.23
+
+        results_df = pd.DataFrame({
+            'Date': future_dates,
+            'Predicted_Consumption': predictions,
+            'Month': future_dates.month,
+            'Year': future_dates.year
+        })
+
+        energy_cost = results_df['Predicted_Consumption'] * ENERGY_RATE
+        distribution_fee = energy_cost * DISTRIBUTION_MULTIPLIER
+        subtotal = energy_cost + distribution_fee
+        vat = subtotal * VAT_RATE
+        results_df['Predicted_Cost'] = subtotal + vat
+
+        return results_df
+
+    def _predict_baseline(self, months):
+        last_date = self.data_stats['max_date']
+        future_dates = pd.date_range(start=last_date + pd.DateOffset(months=1), periods=months, freq='MS')
+
+        predictions = []
+        for date in future_dates:
+            seasonal_pred = self.baseline_predictions.get(date.month, self.data_stats['mean_consumption'])
+            predictions.append(max(0, seasonal_pred * (1 + np.random.normal(0, 0.1))))
+
+        ENERGY_RATE = 0.6972
+        DISTRIBUTION_MULTIPLIER = 0.5068  
+        VAT_RATE = 0.23
+
+        results_df = pd.DataFrame({
+            'Date': future_dates,
+            'Predicted_Consumption': predictions,
+            'Month': future_dates.month,
+            'Year': future_dates.year
+        })
+
+        energy_cost = results_df['Predicted_Consumption'] * ENERGY_RATE
+        distribution_fee = energy_cost * DISTRIBUTION_MULTIPLIER
+        subtotal = energy_cost + distribution_fee
+        vat = subtotal * VAT_RATE
+        results_df['Predicted_Cost'] = subtotal + vat
+
+        return results_df
+
+    def get_feature_importance(self):
+        if hasattr(self.best_model, 'feature_importances_'):
+            importance_dict = dict(zip(self.feature_columns, self.best_model.feature_importances_))
+            return dict(sorted(importance_dict.items(), key=lambda x: x[1], reverse=True))
+        return {}
+
+    def save_model(self, filepath=None, format='joblib'):
+        if self.best_model is None:
+            raise ValueError("Model must be trained first. Use train() method.")
+
+        if filepath is None:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            extension = 'joblib' if format == 'joblib' else 'pkl'
+            filepath = f"energy_model_{self.best_model_name}_{timestamp}.{extension}"
+
+        os.makedirs(os.path.dirname(filepath) if os.path.dirname(filepath) else '.', exist_ok=True)
+
+        model_data = {
+            'best_model': self.best_model,
+            'best_model_name': self.best_model_name,
+            'scaler': self.scaler,
+            'feature_columns': self.feature_columns,
+            'data_stats': self.data_stats,
+            'models': self.models,
+            'baseline_predictions': getattr(self, 'baseline_predictions', None),
+            'metadata': {
+                'saved_at': datetime.now().isoformat(),
+                'model_type': self.best_model_name,
+                'feature_count': len(self.feature_columns) if self.feature_columns else 0
+            }
+        }
+
+        if format == 'joblib':
+            joblib.dump(model_data, filepath)
+        else:
+            with open(filepath, 'wb') as f:
+                pickle.dump(model_data, f)
+
+        return filepath
+
+    def load_model(self, filepath, format='auto'):
+        if not os.path.exists(filepath):
+            raise FileNotFoundError(f"File {filepath} does not exist.")
+
+        if format == 'auto':
+            if filepath.endswith('.joblib'):
+                format = 'joblib'
+            elif filepath.endswith('.pkl'):
+                format = 'pickle'
+            else:
+                format = 'joblib'
+
+        try:
+            if format == 'joblib':
+                model_data = joblib.load(filepath)
+            else:
+                with open(filepath, 'rb') as f:
+                    model_data = pickle.load(f)
+
+            self.best_model = model_data['best_model']
+            self.best_model_name = model_data['best_model_name']
+            self.scaler = model_data['scaler']
+            self.feature_columns = model_data['feature_columns']
+            self.data_stats = model_data['data_stats']
+            self.models = model_data['models']
+            self.baseline_predictions = model_data.get('baseline_predictions')
+
+        except Exception as e:
+            raise ValueError(f"Error loading model: {str(e)}")
+
+    @classmethod
+    def from_file(cls, filepath, format='auto'):
+        model = cls()
+        model.load_model(filepath, format)
+        return model

requirements.txt

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+pandas>=2.0.0
+scikit-learn>=1.3.0
+numpy>=1.24.0
+joblib>=1.3.0