Short-term photovoltaic power generation prediction based on VMD-IGWO-LSTM
Abstract
In order to improve the prediction accuracy of photovoltaic power generation, a short-term photovoltaic power generation prediction method based on variational mode decomposition (VMD) and improved grey wolf optimization algorithm (IGWO) to optimize long short term memory (LSTM) neural network is proposed. The multi-dimensional photovoltaic feature data is decomposed into several intrinsic modes and residual components of different frequencies by VMD algorithm to reduce the non-stationarity of the original sequence; IGWO is used to globally optimize the hyperparameters of LSTM neural network, and the IGWO-LSTM combination model under different modal sequence components is established; the trained combination model is used to perform multi-dimensional prediction of the modal feature components of each decomposed subsequence, and the prediction results of each modal component are summed and reconstructed as the final prediction result. The actual data of a photovoltaic power generation are used for experimental analysis. The simulation results show that the constructed VMD-IGWO-LSTM combination model has better prediction effect than the conventional short-term photovoltaic power generation prediction model, which verifies that the method has higher prediction accuracy.