融合物理规律与可解释性的电网负荷预测模型，提升极端天气下预测可信度。
Interpretable Physics-Informed Load Forecasting for U.S. Grid Resilience: SHAP-Guided Ensemble Validation in Hybrid Deep Learning Under Extreme Weather
- CNN+Transformer双分支融合，结合物理约束损失提升预测精度。
- 极端天气下预测误差降低超40%，关键指标优于单一模型。
- 使用SHAP分析揭示气象变量影响随气候状态动态变化。
准确的短期电力负荷预测是美国电网可靠性的基石；然而，现有深度学习模型缺乏可解释性，限制了极端天气下运维人员的信任。本文提出一种统一、可解释且融合物理知识的集成框架，结合卷积神经网络（CNN）提取局部特征与变压器（Transformer）建模长程依赖，通过验证优化的加权集成融合，并以德克萨斯州电力可靠性委员会（ERCOT）系统的分段抛物线温-需关系为基础设计物理信息损失函数进行正则化。采用后验可解释性方法，基于DeepExplainer实现的SHAP分析，获得全局及事件级贡献度。利用2018-2025年八年间每小时的ERCOT负荷数据与三个德州站点的自动表面观测系统（ASOS）记录进行训练与测试，在测试窗口上实现713兆瓦（MW）MAE、812兆瓦RMSE和1.18% MAPE。对于Hampel标记的极端事件，相对于其Transformer分支和CNN分支，相对MAPE分别下降20.7%和40.5%；消融实验表明，抛物线及爬坡约束带来14.7%的RMSE下降。SHAP分析揭示模式转变：正常运行时温度主导，而在冷锋与热浪期间风速与降水影响显著增强。
原文摘要 · Abstract (English)
Accurate short-term electricity load forecasting is a cornerstone of U.S. grid reliability; however, prevailing deep learning models remain opaque, limiting operator trust during extreme weather. A unified, interpretable, physics-informed ensemble framework is proposed, integrating a Convolutional Neural Network (CNN) branch for local feature extraction and a Transformer branch for long-range dependency modeling; the branches are fused through a validation-optimized weighted ensemble and regularized by a physics-informed loss derived from the piecewise parabolic temperature-demand relationship of the Electric Reliability Council of Texas (ERCOT) system. Post-hoc interpretability is provided through SHapley Additive exPlanations (SHAP) with the DeepExplainer backend, yielding global and event-level attributions. Using eight years of ERCOT hourly load data (2018-2025) fused with Automated Surface Observing System (ASOS) records from three Texas stations, the framework achieves 713 MW MAE, 812 MW RMSE, and 1.18% MAPE on the test window. For Hampel-flagged extreme events, MAPE falls by 20.7% relative to its Transformer branch and by 40.5% relative to its CNN branch; an ablation confirms that the parabolic and ramp constraints drive a 14.7% RMSE reduction. SHAP analysis reveals a regime shift: temperature dominates under normal operation, whereas wind speed and precipitation become more influential during cold fronts and heatwaves.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。