Capacity evaluation and prediction method based on multi-model adaptation for distributed photovoltaic system

Aiming at the challenges brought by the strong fluctuation and uncertainty of power output of distributed photovoltaic (DPV) system to power grid operation, and the limitations of existing capacity evaluation and prediction methods in complex scenarios, this paper proposes a multi-model adaptive DPV capacity evaluation and prediction method. Firstly, the method constructs a multi-model library including physical mechanism model, time series statistical model and machine learning model to realize complementary advantages under different working conditions. Then, a dynamic weight fusion algorithm based on entropy weight method is adopted to dynamically allocate weights according to the prediction errors of each model in the sliding window, so as to avoid the subjectivity of traditional methods. At the same time, the online updating mechanism driven by sliding window is designed, and the model parameters and fusion weights are continuously optimized through incremental training or periodic retraining to adapt to the dynamic changes of system state and environment. Experimental results show that the proposed method outperforms traditional single models and static weight fusion methods in terms of metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Normalized Mean Absolute Error (NMAE). It can effectively improve the prediction accuracy and robustness under different meteorological conditions. Moreover, it can achieve theoretical capacity assessment, actual performance evaluation, and dynamic capacity assessment with early fault warning for DPV systems, providing technical support for the efficient utilization of DPV and the coordinated control of source-grid-load-storage.