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 improve the utilization rate of photovoltaic power generation. Therefore, this paper puts forward an ultra-short-term power forecasting model of photovoltaic power station based on modal decomposition and deep learning. Methodology involved taking the data of a 50MW photovoltaic power generation system in Inner Mongolia Autonomous Region as a sample. Further, the weather conditions were classified, and the historical power data was decomposed into multiple VMF subcomponents and residual terms by VMD method. Then, the residual term was decomposed twice by CEEMDAN method. All sub-components were sent to LSTM network for prediction, and the predicted value of photovoltaic power station was obtained by superimposing the sub-component prediction results. ARIMA, SVM, LSTM, VMD－LSTM models were built to compare the accuracy with the proposed models. The results revealed that the prediction accuracy of 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 compared with VMD－LSTM model, the eRMSE, eMAPE and eTIC of 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 improvess the precision of ultra-short-term prediction of photovoltaic power plants.