Predicting short wind speed with a hybrid model based on a piecewise error correction method and Elman neural network

Abstract

The effective utilization of wind energy is a positive development trend in renewable energy that provides an effective impetus to alleviate global warming. However, the complex volatility and extreme suddenness of wind speeds make it difficult to accurately predict wind speeds. To overcome this challenge, a novel short-term wind speed prediction model based on double decomposition, piecewise error correction, Elman neural network and the autoregressive integrated moving average model is proposed. In this model, the fuzzy entropy is applied to judge the complexity of each subseries generated by variational mode decomposition. To address error correction, a piecewise error correction method that needs to extract only a part of the error sequence as a training set is proposed. Three experiments utilizing 6 Datasets and 14 compared models are conducted to verify the effectiveness of the proposed model. The results show that (i) the hybrid strategy based on double decomposition and piecewise error correction can improve the final prediction accuracy of different single models; (ii) the proposed hybrid model can effectively handle both linear and nonlinear problems; (iii) the root mean squared error of the proposed model for Dataset1-6 in seven-step forecasting are 0.1243, 0.1444, 0.0233, 0.1015, 0.1846 and 0.2513, respectively.