A Comparative Study of Machine Learning Algorithms for Electricity Price Forecasting with LIME-Based Interpretability

TL;DR

This paper compares eight machine learning models for electricity price forecasting using Spanish market data, highlighting KNN's superior performance and employing LIME for interpretability of influential factors.

Contribution

It introduces a comprehensive comparison of ML models for electricity price prediction and applies LIME to interpret model decisions, enhancing transparency.

Findings

KNN achieved the highest accuracy with R^2 of 0.865.

Meteorological and supply-demand variables significantly influence prices.

LIME analysis provides insights into nonlinear relationships affecting prices.

Abstract

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.