A Physics-Aware and SHAP-Guided Adaptive Method for PV Power Forecasting 物理感知与 SHAP 引导的自适应光伏功率预测方法
- 纯数据驱动光伏功率预测在夜间或低辐照段常输出负功率，违背物理常识，影响电网调度与场站运维决策可信度。在 Autoformer、FEDformer、Informer 等 SOTA 时序骨干中引入非对称非负损失，显式抑制非物理负功率输出并完成多骨干对照实验，DKASC 上负功率显著减少、基线 MSE 稳定改善。
- 黑箱调参易使模型过度依赖全球水平辐照度 GHI 等少数特征，过拟合与贡献漂移难以提前发现。搭建 SHAP 对 GHI 贡献的监测流程，并接入 TPE 贝叶斯超参搜索，使超参区间更贴合物理因果，训练中期可识别贡献异常与过拟合迹象，提升可诊断性与跨季节稳定性。
- 公开数据存在季节与工况切换带来的概念漂移，全量重训练在线成本高。实现 SimpleADWIN 漂移检测触发机制，采用冻结骨干并微调末端投影层的轻量在线更新策略，相对全量重训练在线适应耗时缩短约 92%，SOTA 模型 MSE 平均改善约 17%，形成物理—解释—演化一体化结论。
摘要
Deep learning models for photovoltaic (PV) power forecasting often face challenges such as black-box opacity, physical law violations, and performance decay caused by concept drift. This research develops an adaptive forecasting framework guided by physics and Shapley additive explanations (SHAP). The proposed framework integrates domain physics with data-driven evolution. We use state-of-the-art (SOTA) forecasting architectures, including Autoformer, FEDformer, and Informer, as the backbone. We embed an asymmetric non-negative penalty into the loss functions to eliminate irrational negative power predictions. For diagnostic transparency, a SHAP-based introspection module monitors the global horizontal irradiance (GHR) contribution ratio to identify feature overfitting. This feedback directs a Bayesian optimization loop via the tree-structured Parzen estimator (TPE) algorithm to align model weights with physical causality. To ensure robustness in non-stationary environments, a lightweight evolution module triggered by SimpleADWIN freezes the SOTA backbone and fine-tunes the terminal projection layer. Validations on the Desert Knowledge Australia Solar Centre (DKASC) dataset demonstrate that the framework rectifies causal biases effectively. The method yields a 17% mean squared error improvement for SOTA models and accelerates online adaptation by 92% compared to global retraining.