Hybrid neural network wind speed prediction based on two-level decomposition and weighted averaging

The randomicity and fluctuation of the wind speed will influence the precision of the forecast. This paper presents a new method of combined wind speed forecast based on the two-level decomposition and weighted average, which can improve the accuracy of wind speed forecasting. First, the improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) decomposition method is used to get different sub-sequences, and then the fuzzy entropy is used to judge the degree of confusion of the sub-sequences. In this paper, the autoregressive integrated moving average (ARIMA) model is used to predict the minimum fuzzy entropy. And the other subsequences are decomposed by backpropagation neural network (BPNN), variational mode decomposition (VMD) and predicted by nonlinear auto-regressive (NAR) and BP neural network with suitable weighting ratio for weighted average and particle swarm optimization- long short-term memory (PSO-LSTM) neural network respectively, and ultimately all the predicted values are superimposed to get the final prediction. Experiments are conducted using three datasets and eight comparison models to verify the validity of this model. The prediction analysis was carried out using the actual measured data of a wind farm in Inner Mongolia, and the results indicated that (1) using fuzzy entropy can effectively improve the prediction precision; (2) the prediction accuracy of the combined prediction method of neural network based on two-level decomposition was greatly improved and the prediction results were more reliable; (3) Decompose one of the subsequences with VMD, predict it with NAR and BP neural network, and choose appropriate weight ratio for weighted average prediction will achieve better prediction results; (4) the root mean square error (RMSE) of the hybrid model on the three wind speed datasets were 0.28777, 0.22786 and 0.17128, which are lower than the comparison values of other models. So, it is workable to use this hybrid model in wind speed prediction.