Research on Integrating Physical Constraints with HO-Transformer-KAN for Short-Term Photovoltaic Power Forecasting

To address the issues of limited interpretability and low predictive accuracy in traditional photovoltaic forecasting models, this paper proposes a hybrid forecasting model named HO-Transformer-KAN-PINN. First, Maximal Information Coefficient (MIC) is used to select the key meteorological features: irradiance and temperature. Then, the grey relational analysis combined with cosine similarity is applied to identify similar days. The prediction framework is then constructed. The Transformer-KAN model provides high predictive accuracy and strong interpretability, while embedding physics-informed neural network (PINN) constraints enforces compliance with the underlying physical laws, yielding the Transformer-KAN-PINN framework. Simultaneously, the Hippopotamus Optimization (HO) algorithm is used to optimize the model hyperparameters. Finally, the photovoltaic power combination prediction model of HO-Transformer-KAN-PINN is constructed. This model has achieved excellent results in short-term photovoltaic power forecasting in Yunnan, Gansu, and Australia. Taking winter in Yunnan Province as an example, the forecasting results of this model yield an MAE of 0.3204 MW, an RMSE of 0.4197 MW, a MAPE of 4.9561%, and an R2 of 0.9986. Therefore, the hybrid forecasting model proposed in this paper demonstrates a certain degree of advancement and effectiveness. Therefore, it provides reliable technical support for accurate prediction of photovoltaic output.