Abstract
Accurate wind speed forecasting is an effective way to improve the safety and stability of power grid. A novel hybrid model based on twice decomposition, phase space reconstruction (PSR), and an improved multiverse optimizer-extreme learning machine (IMVO-ELM) is proposed to enhance the performance of short-term wind speed forecasting in this paper. In consideration of the nonstationarity of the wind speed signal, a twice decomposition based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), fuzzy entropy, and variational mode decomposition (VMD) is proposed to reduce the nonstationarity of the original signal firstly. Then the PSR based on C-C method is employed to reconstitute the decomposed signal as the input of the prediction model. Lastly, an improved multiverse optimizer is proposed to improve the stability and efficiency of ELM which is used as prediction model. Furthermore, two experiments are designed to verify the performance of the proposed method; the results indicate that (1) the wind speed forecasting with twice decomposition of original wind speed signal is better than other once-decomposition methods and much better than forecasting without decomposition; (2) the C-C-PSR method can determine the input dimension of ELM and improve the prediction accuracy of ELM; (3) the IMVO has improved the stability of ELM, and the optimization efficiency is better than other comparison optimization methods. The results show that the proposed hybrid approach is a useful tool for short-term wind speed forecasting.
Highlights
With exhaustion of fossil energy and increase of requirements of environmental protection, energy supply has become an important problem
A prediction model based on C-C-phase space reconstruction (PSR) and improved multiverse optimizer (IMVO)-extreme learning machine (ELM) is proposed as prediction module. e main contributions of this paper are illustrated as follows: (1) A twice decomposition based on ICEEMDAN, fuzzy entropy, and variational mode decomposition (VMD) is proposed for wind speed signal to improve the prediction accuracy. e ICEEMDAN is utilized to the original wind speed signal firstly
A novel hybrid model based on twice decomposition, PSR, and IMVO-ELM is proposed to enhance the performance of short-term wind speed forecasting
Summary
With exhaustion of fossil energy and increase of requirements of environmental protection, energy supply has become an important problem. E ARIMA model can convert nonstationary signals into stationary time series which improved the prediction accuracy of wind speed [12]. A novel hybrid model for short-term wind speed forecasting based on twice decomposition, phase space reconstruction (PSR), and an improved multiverse optimizer-extreme learning machine (IMVO-ELM) is proposed. A twice-decomposition method based on ICEEMDAN, fuzzy entropy, and VMD is proposed as data processing module. (1) A twice decomposition based on ICEEMDAN, fuzzy entropy, and VMD is proposed for wind speed signal to improve the prediction accuracy. 2. Theoretical Background e theoretical backgrounds related to the proposed method of this paper are briefly reviewed including ICEEMDAN, VMD, fuzzy entropy, PSR based on C-C, and ELM. E β can be determined by min‖Hβ − Y‖2
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