用可解释性指导特征工程，提升中长期电力负荷预测精度
Explainability-Driven Feature Engineering for Mid-Term Electricity Load Forecasting in ERCOT's SCENT Region
- 引入SHAP分析特征贡献，优化特征工程
- 在1年内预测中，模型准确率显著提升
- 适合电力系统规划与运维决策者参考
精准的负荷预测对现代电力系统运行至关重要。由于电力需求对天气变化和时间动态敏感，捕捉非线性模式对长期规划尤为关键。本文对比了线性回归、XGBoost、LightGBM和长短期记忆网络（LSTM）在预测埃克顿（ERCOT）SCENT区域全年系统级用电负荷方面的表现。中长期预测对维护调度、资源分配、财务预测和市场参与具有重要意义。研究重点采用“基于加法的解释方法”（SHAP）提升模型可解释性，量化各特征贡献，指导特征工程，从而增强模型透明度并提高预测准确性。
原文摘要 · Abstract (English)
Accurate load forecasting is essential to the operation of modern electric power systems. Given the sensitivity of electricity demand to weather variability and temporal dynamics, capturing non-linear patterns is essential for long-term planning. This paper presents a comparative analysis of machine learning models, Linear Regression, XGBoost, LightGBM, and Long Short-Term Memory (LSTM), for forecasting system-wide electricity load up to one year in advance. Midterm forecasting has shown to be crucial for maintenance scheduling, resource allocation, financial forecasting, and market participation. The paper places a focus on the use of a method called "Shapley Additive Explanations" (SHAP) to improve model explainability. SHAP enables the quantification of feature contributions, guiding informed feature engineering and improving both model transparency and forecasting accuracy.
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