Ultra-Short-Term Power Prediction of a Photovoltaic Power Station Based on the VMD-CEEMDAN-LSTM Model - Wang, Shuaijie; Liu, Shu; Guan, Xin (Frontiers in Energy Research, 2022)
The prediction of photovoltaic power generation is helpful to the overall allocation of power planning departments and improves the utilization rate of photovoltaic power generation. Therefore, this study puts forward an ultra-short-term power forecasting model of a photovoltaic power station based on modal decomposition and deep learning. The methodology involved taking the data of a 50 MW photovoltaic power generation system in the Inner Mongolia Autonomous Region as a sample. Furthermore, the weather conditions were classified, and the historical power data were decomposed into multiple VMF subcomponents and residual terms by the VMD method. Then, the residual term was decomposed twice by the CEEMDAN method. All subcomponents were sent to the LSTM network for prediction, and the predicted value of the photovoltaic power station was obtained by superimposing the subcomponent prediction results. ARIMA, SVM, LSTM, and VMD-LSTM models were built to compare the accuracy with the proposed models. The results revealed that the prediction accuracy of a non-combination forecasting model was limited when the weather suddenly changed. The VMD method was used to decompose the residual term twice, which could fully extract the complex data information in the residual term, and when compared with the VMD-LSTM model, the eRMSE, eMAPE, and eTIC of the VMD-CEEMDAN-LSTM model were reduced by 0.104, 16.596, and 0.038, respectively. The second decomposition technology has obvious prediction advantages. The proposed quadratic modal decomposition model effectively improves the precision of ultra-short-term prediction of photovoltaic power plants.