Forecasting electricity prices is crucial for efficient energy market operations. This project applies machine learning models to historical data on energy production and weather conditions to predict electricity prices.
Accurate price forecasting is key to optimizing energy production, managing grid stability, and facilitating effective market trading. The challenge is to predict future electricity prices based on historical data. The project leverages time-series analysis and forecasting techniques using datasets related to energy production and weather conditions.
- Energy Dataset: Contains historical data on energy production from various sources like biomass, solar, and wind.
- Weather Dataset: Includes weather conditions data, which significantly influence energy production and consumption patterns.
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Data Cleaning:
- Handling missing values and outliers in both energy and weather datasets.
- Converting timestamps to a suitable format for time-series analysis.
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Modeling:
- Feature Engineering: Creating new features relevant to electricity price prediction.
- Model Selection: Evaluating different machine learning models, including XGBoost and time-series models like ARIMA.
- Performance Metrics: Using metrics like Mean Squared Error (MSE) and R-squared to evaluate model accuracy.