Multi-step wind speed forecasting and Hurst analysis using novel hybrid secondary decomposition approach

Wind speed should be predicted in a sensitive and reliable manner for the effective use of wind energy in wind farms. However, the volatility and non-linearity features of wind make it difficult to do so. Hence, many researchers have focused on the development of reliable prediction models for wind speed. Aimed at this challenge, the present study proposes a hybrid model comprised of Ensemble Empirical Mode Decomposition Adaptive Noise (CEEMDAN), Local Mean Decomposition (LMD), Hurst and Back-propagation Neural Network (BPNN). This model is actualized as follows: First, wind speed time series is decomposed into its sub-components via CEEMDAN technique. The least irregular and unsystematic of the IMFs with the highest frequency obtained as a result of decomposition via CEEMDAN is subject to secondary decomposition using the LMD technique. The obtained components are subject to Hurst analysis to be transformed into micro, meso and macro scale series. These series are then applied on feedback artificial neural networks. The analysis results indicate that model proposed has a better per-formance than the compared traditional forecasting methods (EEMD-VDM-BPNN and EEMD-EWT-BPNN) with regard to prediction accuracy. The MAPE values obtained via the proposed hybrid model were observed to have decreased by 41.16% and 78.80% when compared with those obtained using traditional models. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

A hybrid model combining CEEMDAN, LMD, Hurst, and BPNN is proposed for wind speed prediction, showing better accuracy compared to traditional methods with a decrease in MAPE values by 41.16% and 78.80%.