用机器学习预测电价，还用LIME解释影响因素
A Comparative Study of Machine Learning Algorithms for Electricity Price Forecasting with LIME-Based Interpretability
- 对比8种模型，用气象与供需数据预测电价
- KNN表现最优，R²达0.865，误差低于5.24
- 用LIME揭示气象和供需是非线性影响关键
随着电力市场快速发展，电价波动加剧，准确预测对系统运行和市场决策至关重要。传统线性模型难以捕捉电价的非线性特征，需采用先进机器学习方法。本研究基于西班牙电力市场数据，比较了八种机器学习模型，输入包含用电量、发电量及气象变量。评估模型包括线性回归、岭回归、决策树、KNN、随机森林、梯度提升、支持向量回归（SVR）和XGBoost。结果表明，KNN表现最佳，R²为0.865，平均绝对误差（MAE）为3.556，均方根误差（RMSE）为5.240。为进一步提升可解释性，采用LIME分析发现，气象因素和供需指标通过非线性关系显著影响价格波动。本工作验证了机器学习在电价预测中的有效性，并通过可解释性分析增强了决策透明度。
原文摘要 · Abstract (English)
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.
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