在全球气候发生显著变化的背景下，极端天气频发，严重影响新能源电力系统的安全稳定运行。现有光伏功率在线学习算法未考虑极端天气数据对模型性能的影响。考虑到极端天气下的光伏数据分布和常规天气存在极大差异，在线学习时如果新数据中包含了极端天气数据，直接使用极端天气数据对模型进行训练可能导致模型常规天气下的预测性能大幅度下降，即出现“灾难性遗忘”。针对上述问题，本文提出了一种考虑极端天气的光伏功率预测在线学习算法：首先基于D-EMA算法识别极端天气数据；然后基于Online-EWC算法实现常规天气数据的在线学习，得到基准预测模型；最后使用BOWM-EWC算法在梯度和损失函数两个维度的约束下基于极端天气数据对模型进行微调，以减少极端天气数据对常规天气下模型预测性能的影响，并将极端天气数据纳入到在线学习的框架中。算例说明本文所提算法能够在提升极端天气数据预测精度的同时有效缓解极端天气数据对常规天气下模型预测性能的影响，证明了本文所提算法的有效性。

Amidst the backdrop of significant global climate change, the frequent occurrence of extreme weather has severely impacted the secure and stable operation of renewable energy power systems. Existing online learning algorithms for photovoltaic (PV) power forecasting do not account for the influence of extreme weather data on model performance. Given the substantial differences in the distribution of PV data between extreme and normal weather conditions, direct training with extreme weather data during online learning—when such data is included in the new data stream—can lead to a significant decline in the model’s forecasting performance under normal weather conditions, a phenomenon known as "catastrophic forgetting." To address this issue, this paper proposes an online learning algorithm for PV power forecasting that considers extreme weather. First, the D-EMA algorithm is employed to identify extreme weather data. Subsequently, the Online-EWC algorithm is utilized for online learning with normal weather data to establish a baseline forecasting model. Finally, the BOWM-EWC algorithm is applied to fine-tune the model using extreme weather data, incorporating constraints at both the gradient and loss function levels. This approach aims to mitigate the adverse effects of extreme weather data on the model’s forecasting performance under normal conditions while integrating extreme weather data into the online learning framework. Case studies demonstrate that the proposed algorithm not only improves forecasting accuracy for extreme weather data but also effectively alleviates the detrimental impact of such data on model performance under normal weather conditions, thereby validating its effectiveness.