PV Power Short-Term Forecasting Method Based on VMD-GWO-ELMAN - Na ZHANG; Qiang REN; Guangchen LIU; Liping GUO; Jingyu LI (Zhongguo dianli)
This paper aims to further improve the accuracy and reliability of short-term photovoltaic (PV) output power forecasting. Considering the blindness and randomness of weights and thresholds of traditional Elman neural networks and the fluctuation and nonstationarity of PV output power signal, the paper proposes a short-term prediction model of PV output power based on variational mode decomposition (VMD) and an Elman neural network optimized by grey wolf optimization (GWO) algorithm. Firstly, the K-means algorithm is used to cluster the original data according to weather types. Then, VMD is employed to decompose the PV output power data of each weather type, and the decomposition subsequences are input into the Elman neural network optimized by GWO for PV output power forecasting. Finally, the forecasting results are superimposed. An example shows that the model has improved forecasting accuracy.