电力系统保护与控制2026，Vol.54Issue(2)：58-69,12.DOI:10.19783/j.cnki.pspc.250237
物理特征扩展的ASReLU-CNN-LSTM短期光伏功率预测研究
Research on short-term photovoltaic power forecasting based on a physical feature expansion ASReLU-CNN-LSTM model
摘要
Abstract
To enhance the accuracy and stability of photovoltaic(PV)power output forecasting under complex and highly variable meteorological conditions,a physics-data fusion-driven strategy is adopted,and a physical feature expansion ASReLU-CNN-LSTM method for short-term PV power forecasting is proposed.First,an improved solar trajectory model is used to dynamically correct the tilted surface irradiance so that it accurately reflects the actual irradiance received by PV modules.Subsequently,a PV conversion model and a lightweight feedforward network are employed to expand the dataset with relative power features.An adaptively smooth rectifier linear unit(ASReLU)is then designed,in which parameterized adaptive smoothing is introduced to enhance the negative-feature extraction capability of the convolutional neural network(CNN).Finally,the dataset augmented with physical features is fed into the ASReLU-CNN-LSTM model for PV power prediction.Experimental results on datasets from two distinct climatic regions demonstrate that the proposed method achieves high prediction accuracy and strong generalization capability.关键词
短期光伏功率预测/太阳轨迹模型/光电转换模型/自适应平滑修正线性单元/CNN-LSTM模型Key words
short-term photovoltaic power forecasting/solar trajectory model/photovoltaic conversion model/adaptively smooth rectified linear unit/CNN-LSTM model引用本文复制引用
刘伟,李洋洋..物理特征扩展的ASReLU-CNN-LSTM短期光伏功率预测研究[J].电力系统保护与控制,2026,54(2):58-69,12.基金项目
This work is supported by the National Natural Science Foundation of China(No.62473096). 国家自然科学基金项目资助(62473096) （No.62473096）
