Weather-Based Energy Prediction System

A comprehensive machine learning system that predicts solar and wind energy generation based on real-time weather data, following the flowchart process outlined in the project requirements

🌟 System Overview

This system implements a complete weather-based energy prediction pipeline that:

1. Fetches real-time weather data from Open-Meteo API
2. Cleans and processes historical CSV data for model training
3. Trains machine learning models for solar and wind energy prediction
4. Generates hourly energy predictions using real-time weather inputs
5. Provides comprehensive model evaluation and performance analysis

🏗️ System Architecture

The system follows the exact process flow from the flowchart:

API (Real-time Weather Data) → CSV Update → Data Cleaning → Model Training → Energy Prediction
     ↓
Weather Parameters: Temperature, Wind Speed, Solar Irradiance, Humidity, Cloud Cover
     ↓
Hourly Energy Predictions: Solar (kW/h) + Total Energy (kW/h)

📁 Project Structure

weather-prediction-model/
├── Data/
│   ├── weather_last_year_data .csv          # Historical training data
│   ├── cleaned_weather_data.csv             # Processed historical data
│   └── weather.csv                          # Additional weather data
├── weather_forecast.py                      # Original weather data fetcher
├── weather_energy_prediction_model.py       # Main prediction model
├── data_preprocessing.py                    # Data cleaning and preprocessing
├── model_evaluation.py                      # Model performance evaluation
├── requirements.txt                         # Python dependencies
└── README.md                               # This file


## 🚀 Quick Start


### 1. Install Dependencies


```bash

pip install -r requirements.txt

2. Run the Complete Pipeline

python weather_energy_prediction_model.py

This will:

• Fetch real-time weather data from API
• Clean historical CSV data
• Train solar and wind energy models
• Generate hourly energy predictions
• Save the trained model

3. Run Data Preprocessing (Optional)

python data_preprocessing.py

This will:

• Clean and analyze historical data
• Create additional features
• Generate data distribution visualizations
• Save cleaned data for model training

4. Evaluate Model Performance (After Training)

python model_evaluation.py

This will:

• Load the trained model
• Evaluate performance metrics
• Generate performance visualizations
• Create a comprehensive performance report

🔧 System Components

1. Main Prediction Model (weather_energy_prediction_model.py)

Class: WeatherEnergyPredictionModel

Key Methods:

• fetch_realtime_weather_data(): Fetches weather data from Open-Meteo API
• clean_csv_data(): Cleans historical CSV data
• train_model(): Trains Random Forest models for solar and wind energy
• predict_energy(): Makes energy predictions using real-time weather data
• run_complete_pipeline(): Executes the complete workflow

Features:

• Dual model approach (solar + wind energy)
• Real-time weather data integration
• Automatic feature engineering
• Model persistence and loading

2. Data Preprocessing (data_preprocessing.py)

Key Functions:

• load_and_explore_data(): Loads and explores historical data
• clean_data(): Removes missing values, duplicates, and outliers
• create_features(): Creates interaction and efficiency features
• encode_categorical_variables(): Encodes categorical variables
• analyze_data_distribution(): Creates data distribution visualizations
• analyze_correlations(): Creates correlation heatmaps

3. Model Evaluation (model_evaluation.py)

Class: ModelEvaluator

Key Methods:

• evaluate_model_performance(): Calculates performance metrics
• create_performance_visualizations(): Creates prediction vs actual plots
• create_feature_importance_plot(): Shows feature importance analysis
• generate_performance_report(): Creates comprehensive performance report

📊 Data Requirements

Input Data Format

The system expects historical data with the following columns:

Column	Description	Type
Source_Type	Energy source (Solar/Wind)	Categorical
Solar_Irradiance	Solar radiation intensity (W/m²)	Numerical
Wind_Speed	Wind speed (m/s)	Numerical
Ambient_Temperature	Air temperature (°C)	Numerical
Humidity	Relative humidity (%)	Numerical
Cloud_Cover	Cloud coverage (%)	Numerical
Panel_Area	Solar panel area (m²)	Numerical
Blade_Length	Wind turbine blade length (m)	Numerical
Storage_Capacity	Energy storage capacity	Numerical
Maintenance_Schedule	Maintenance schedule	Numerical
Energy_Output_Class	Energy output classification	Categorical

Real-time Weather Data

The system fetches real-time weather data including:

• Temperature (°C)
• Wind Speed (m/s)
• Solar Irradiance (W/m²)
• Humidity (%)
• Cloud Cover (%)

🎯 Model Architecture

Algorithm

• Random Forest Regressor for both solar and wind energy prediction
• Separate models for solar and wind energy generation
• Feature scaling using StandardScaler
• Categorical encoding using LabelEncoder

Features

• Base Features: Solar_Irradiance, Wind_Speed, Ambient_Temperature, Humidity, Cloud_Cover, Panel_Area, Blade_Length
• Engineered Features: Temperature_Squared, Humidity_Temperature_Interaction, Solar_Wind_Interaction
• Categorical Features: Source_Type_Encoded

Target Variables

• Solar Energy: Solar_Irradiance × Panel_Area × Efficiency_Factor
• Wind Energy: Wind_Speed³ × Blade_Length × Power_Coefficient

📈 Output and Results

Energy Predictions

The system generates hourly predictions in the format:

Datetime	Predicted_Solar_kWh	Predicted_Energy_kWh
2024-01-15 09:00	25.0	35.0
2024-01-15 10:00	40.0	50.0
...	...	...

Generated Files

1. Real-time weather data: weather_forecast_YYYY-MM-DD.csv
2. Energy predictions: energy_predictions_YYYYMMDD_HHMMSS.csv
3. Trained model: weather_energy_model.pkl
4. Cleaned data: Data/cleaned_weather_data.csv
5. Visualizations:
   • data_distribution_analysis.png
   • correlation_heatmap.png
   • model_performance_analysis.png
   • feature_importance_analysis.png
6. Reports: model_performance_report.txt

🔄 Workflow Process

Step 1: Data Ingestion

• Fetch real-time weather data from Open-Meteo API
• Update CSV with latest weather information

Step 2: Data Cleaning

• Remove missing values and duplicates
• Handle outliers using IQR method
• Create additional engineered features
• Encode categorical variables

Step 3: Model Training

• Split data into training and testing sets
• Scale features using StandardScaler
• Train Random Forest models for solar and wind energy
• Evaluate model performance

Step 4: Real-time Prediction

• Use trained models with real-time weather data
• Generate hourly energy predictions
• Save predictions to CSV file

🎛️ Configuration

Location Settings

# In weather_energy_prediction_model.py
latitude = 23.077080    # Your location latitude
longitude = 76.85131    # Your location longitude

Model Parameters

# Random Forest parameters
n_estimators = 100      # Number of trees
random_state = 42       # Random seed for reproducibility
test_size = 0.2         # Test set size (20%)

Feature Engineering

# Efficiency factors
solar_efficiency = 0.15     # Solar panel efficiency
wind_power_coefficient = 0.4  # Wind turbine power coefficient

📊 Performance Metrics

The system evaluates models using:

• R² Score: Coefficient of determination
• RMSE: Root Mean Square Error
• MAE: Mean Absolute Error
• MAPE: Mean Absolute Percentage Error

🚨 Troubleshooting

Common Issues

1. API Connection Error

   • Check internet connection
   • Verify API endpoint availability
   • Check latitude/longitude coordinates

2. Data Loading Error

   • Ensure CSV file exists in Data/ folder
   • Check file permissions
   • Verify CSV format matches expected structure

3. Model Training Error

   • Ensure sufficient training data
   • Check for missing values in features
   • Verify feature column names match

4. Memory Issues

   • Reduce dataset size for testing
   • Use smaller Random Forest parameters
   • Process data in chunks

Error Messages

• ❌ Failed to fetch weather data: API connection issue
• ❌ Error cleaning data: Data preprocessing problem
• ❌ Error training model: Model training failure
• ❌ Error making predictions: Prediction generation issue

🔮 Future Enhancements

Planned Features

• Real-time Dashboard: Web interface for live monitoring
• Multiple Weather APIs: Fallback and redundancy
• Advanced ML Models: Deep learning and ensemble methods
• Weather Forecasting: Multi-day predictions
• Energy Optimization: Load balancing and storage optimization

Model Improvements

• Hyperparameter Tuning: Grid search and optimization
• Feature Selection: Automated feature importance analysis
• Cross-validation: K-fold cross-validation for robust evaluation
• Model Ensembling: Combine multiple algorithms

📚 Technical Details

Dependencies

• pandas: Data manipulation and analysis
• numpy: Numerical computing
• scikit-learn: Machine learning algorithms
• requests: HTTP library for API calls
• joblib: Model persistence
• matplotlib: Data visualization
• seaborn: Statistical data visualization

System Requirements

• Python: 3.7 or higher
• Memory: Minimum 4GB RAM
• Storage: 1GB free space
• Internet: Required for API calls

Performance

• Training Time: 1-5 minutes (depending on data size)
• Prediction Time: <1 second per hour
• Memory Usage: 100-500MB during training
• Storage: ~50MB for trained models

🤝 Contributing

Development Setup

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

Code Style

• Follow PEP 8 guidelines
• Add docstrings for all functions
• Include type hints where possible
• Write clear commit messages

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📞 Support

For questions and support:

• Create an issue in the repository
• Check the troubleshooting section
• Review the error logs
• Verify system requirements

🎉 Acknowledgments

• Open-Meteo API for weather data
• Scikit-learn for machine learning algorithms
• Python community for excellent libraries
• Contributors and users of this system

---

Note: This system is designed for educational and research purposes. For production use, please ensure proper testing, validation, and compliance with local regulations.