A Comparative Study of Machine Learning Algorithms for Electricity Price Forecasting with LIME-Based Interpretability
This work addresses the problem of price volatility for electricity market operators and decision-makers, but it is incremental as it applies existing methods to a specific dataset with interpretability analysis.
This study tackled electricity price forecasting by comparing eight machine learning models on Spanish market data, finding that KNN achieved the best performance with an R^2 of 0.865, MAE of 3.556, and RMSE of 5.240, and used LIME to enhance interpretability by identifying key influencing factors.
With the rapid development of electricity markets, price volatility has significantly increased, making accurate forecasting crucial for power system operations and market decisions. Traditional linear models cannot capture the complex nonlinear characteristics of electricity pricing, necessitating advanced machine learning approaches. This study compares eight machine learning models using Spanish electricity market data, integrating consumption, generation, and meteorological variables. The models evaluated include linear regression, ridge regression, decision tree, KNN, random forest, gradient boosting, SVR, and XGBoost. Results show that KNN achieves the best performance with R^2 of 0.865, MAE of 3.556, and RMSE of 5.240. To enhance interpretability, LIME analysis reveals that meteorological factors and supply-demand indicators significantly influence price fluctuations through nonlinear relationships. This work demonstrates the effectiveness of machine learning models in electricity price forecasting while improving decision transparency through interpretability analysis.