A Novel Hybrid Model for Short-Term Wind Speed Forecasting Based on Twice Decomposition, PSR, and IMVO-ELM

Hindawi Complexity Volume 2022, Article ID 4014048, 21 pages https://doi.org/10.1155/2022/4014048 Research Article A Novel Hybrid Model for Short-Term Wind Speed Forecasting Based on Twice Decomposition, PSR, and IMVO-ELM Xin Xia and Xiaolu Wang School of Mechanical and Electrical Engineering, Suqian University, Suqian 223800, China Correspondence should be addressed to Xin Xia; Received 21 October 2021; Accepted 5 January 2022; Published 19 January 2022 Academic Editor: Lingzhong Guo Copyright © 2022 Xin Xia and Xiaolu Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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. 1. Introduction With exhaustion of fossil energy and increase of requirements of environmental protection, energy supply has become an important problem. Developing clean energy is an effective way to solve energy problems. Wind energy as a cheap, recyclable, pollution-free energy has been vigorously developed by many countries, and the capacity of wind turbine is increasing rapidly [1]. According to statistics, the wind-turbine capacity increased from 487 GW in 2016 to 702 GW in 2020 [2]. Wind speed has the characteristics of randomness, intermittence, and fluctuation which makes the output power of wind turbine unstable. With the grid-connected largescale wind power, the unstable output power brings great challenge to power grid [3]. Accurate wind speed forecasting is an effective tool to improve the safety and stability of power grid [4]. Many of wind speed forecasting methods have been proposed in the fast few decades. The methods can be classified into two categories [5]: the physical-driven methods and the data-driven methods. The physical-driven methods are usually established with topography, temperature, density, air pressure, and altitude. And the numerical weather prediction (NWP) is employed for forecasting [6, 7]. With the low resolution of NWP, the physical-driven methods usually cannot meet the demand of short-term wind speed forecasting [8]. The data-driven methods just need the history data for forecasting which is more suitable for short-term wind speed forecasting. The data-driven methods can be divided into two categories: statistical algorithms and artificial intelligence algorithms. The statistical algorithms employed for wind speed forecasting mainly include autoregressive moving average model (ARMA) and autoregressive integrated moving average model (ARIMA) [9, 10]. The ARMA model is a linear model which is not very suitable for the 2 Complexity nonstationary signals [11]. The ARIMA model can convert nonstationary signals into stationary time series which improved the prediction accuracy of wind speed [12]. With the development of computer science, the artificial intelligence algorithms have been widely employed in wind speed forecasting, such as support vector machine (SVM) [13, 14], backpropagation (BP) [15], Elman neural network [16, 17], and extreme learning machine (ELM) [18, 19]. Among these artificial intelligence algorithms, the ELM has the fastest calculation speed and stronger generalization ability [20] which mean it is more suitable for short-term forecasting. With the nonstationarity of wind speed, data preprocessing can get more useful data features from original wind speed signal to improve the prediction accuracy [21, 22]. Data preprocessing methods have been widely used to reduce the nonstationarity of wind speed signal, such as wavelet transform (WT) [12], empirical mode decomposition (EMD) [23], ensemble empirical mode decomposition (EEMD) [24], complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) [25], improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) [26], and variational mode decomposition (VMD) [27]. The ICEEMDAN has solved the modal mixing problem and the residual components in intrinsic mode function (IMF) are greatly reduced [28, 29]. The VMD can also solve the modal mixing problem by decomposing signal into band-limited subseries [30, 31]. In this paper, 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. The proposed method includes data processing module, prediction module, and combination of final results module. A twice-decomposition method based on ICEEMDAN, fuzzy entropy, and VMD is proposed as data processing module. A prediction model based on C-C-PSR and IMVO-ELM is proposed as prediction module. The main contributions of this paper are illustrated as follows: is described in Section 2. In Section 3, the proposed hybrid model and the methodology of the article are described detailedly. Experiments are conducted and the results are analyzed in Section 4. Conclusions are given in Section 5. 2. Theoretical Background The theoretical backgrounds related to the proposed method of this paper are briefly reviewed in this section, including ICEEMDAN, VMD, fuzzy entropy, PSR based on C-C, and ELM. 2.1. ICEEMDAN. ICEEMDAN is proposed by Colominas based on CEEMDAN which is recognized as the important improvement of EEMD [32]. The ICEEMDAN adds the mode of white noise to original signal instead of white noise which greatly reduc (...truncated)