基于VMD-PCA-PINN模型的分布式光伏发电预测研究OA

[Objective]As a key component of the rural energy system,accurate power prediction of distributed photovoltaic(PV)generation is of great significance for improving the renewable energy absorption capacity of rural distribution networks and ensuring the safe and stable operation of rural power grids.Aiming at the problems that PV power sequences present nonlinearity,intermittency and time-varying characteristics,and the insufficient prediction accuracy of traditional models under different weather conditions in rural areas,this paper proposes a combined model named VMD-PCA-PINN,which integrates Variational Mode Decomposition(VMD),Principal Component Analysis(PCA)and Physics-Informed Neural Network(PINN),to solve the short-term power prediction problem under complex meteorological scenarios in rural areas.[Methods]Based on the measured PV data of a rural area in Hebei Province,the dataset was divided into four seasonal subsets and four weather scenarios including sunny,cloudy,rainy and snowy days.Firstly,the non-stationary PV power sequence was decomposed into multiple Intrinsic Mode Functions(IMFs)by VMD technology to reduce random volatility and noise interference.Then,PCA was adopted to reduce the dimension of high-dimensional IMF features and eliminate redundant features.Finally,a PINN network embedded with PV physical constraints was constructed,whose loss function integrates data fitting error and the physical laws of photoelectric conversion to ensure the rationality of prediction results.[Results]Experiments show that the VMD-PCA-PINN model performs excellently in multiple scenarios.Specifically,the R²,MAPE and RPD indices reach 0.972,0.122%and 5.98 respectively on sunny days;0.943,0.297%and 4.19 on rainy days;0.964,0.257%and 5.27 on cloudy days;and 0.931,0.329%and 3.81 on snowy days,demonstrating superior prediction accuracy and robustness.[Conclusion]Through the synergistic effect of signal decomposition,feature optimization and physical constraints,the proposed model significantly improves the short-term prediction performance of rural PV generation,and provides reliable technical support for rural energy optimization.