基于VMD-CNN-BiLSTM-RF模型的短期光伏发电功率预测OA

In response to the non-stationary nature and noise interference caused by weather affected pho-tovoltaic power generation,as well as the spatiotemporal coupling effect,a short-term power prediction composite model VMD-CNN-BiLSTM-RF based on Variational Mode Decomposition(VMD),Convolu-tional Neural Network(CNN),Bi-directional Long Short-Term Memory(BiLSTM),and Random Forest(RF)is proposed.This model uses VMD to decompose the original power sequence into multiple station-ary sub-modes,reducing the impact of noise and non-stationarity.It combines CNN with BiLSTM to sim-ultaneously capture spatial features and temporal dependencies of time series data,improving the accura-cy of predictions.RF is integrated with sub-model prediction results to enhance the model's generalization ability.An experimental environment was set up on the Matlab platform to conduct comparative experi-ments and error analysis for the VMD-CNN-BiLSTM-RF combined model.The results show that this model has significantly improved the accuracy and robustness of short-term photovoltaic power genera-tion prediction.