Machine learning-based price forecasting and risk management in renewable energy markets

The rising share of renewable energy has amplified electricity price volatility, underscoring the need for accurate forecasting and robust risk management. This study proposes an integrated machine learning framework that combines advanced forecasting models (Random Forest, XGBoost, LSTM) with feature engineering and probabilistic risk assessment. Value at risk (VaR) and conditional VaR (CVaR) are derived from predictive distributions to guide hedging strategies using futures and options. Empirical tests on 3 years of hourly data show that the approach reduces RMSE by up to 18% and lowers MAPE below 5%, while improving downside protection by 12% compared with benchmarks. The key contributions are the unification of ML forecasting and economic risk measures, demonstrable gains in both accuracy and resilience, and practical guidance for stakeholders in renewable-intensive markets.