A blended approach incorporating TVFEMD, PSR, NNCT-based multi-model fusion and hierarchy-based merged optimization algorithm for multi-step wind speed prediction. (15th February 2021)

• Record Type: Journal Article
• Authors: Xiong, Dongzhen; Fu, Wenlong; Wang, Kai; Fang, Ping; Chen, Tie; Zou, Feng
• Is Part Of: Energy conversion and management. Volume 230(2021)
• Publication Date: 2021-02-15
• Subjects: Multi-step wind speed prediction -- TVFEMD -- Phase space reconstruction -- NNCT-based multi-model fusion -- Hierarchy-based merged optimization algorithm -- Global elite opposition-based learning; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2020.113680
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: TVFEMD is employed to decrease the non-stationarity of raw wind speed series. Subseries decomposed by TVFEMD are reconstructed by PSR into phase space matrixes. NNCT-based multi-model fusion strategy is applied for wind speed forecasting. GHWOADE is proposed to optimize weight coefficients of single forecasting models. Comparative experiments ascertain the effectiveness of the proposed blended approach. Abstract: Precise wind speed prediction plays an essential role in wind farm planning. To enhance the wind speed forecasting accuracy, a blended approach incorporating time varying filtering-based empirical mode decomposition (TVFEMD), phase space reconstruction (PSR), no negative constraint theory (NNCT)-based multi-model fusion and hierarchy-based merged optimization algorithm is proposed in this paper. Firstly, raw wind speed series is decomposed through TVFEMD into a variety of intrinsic mode functions (IMFs) to decrease its non-stationarity, which are further reconstructed by PSR into input and output vectors of forecasting model. Then, NNCT-based multi-model fusion made up of diverse single forecasting models is established to predict wind speed series, of which the weight coefficients would influence the forecasting results significantly. Subsequently, a hierarchy-based merged optimization algorithm is proposed to optimize the weight coefficients which integrates global elite opposition-based learning strategy and hierarchy-based mechanism with whale optimization algorithm and differential evolution (GHWOADE). Furthermore, the final forecasting results of the raw series are obtained by accumulating the forecasting values of all decomposing subsequences. Eventually, the effectiveness of the proposed approach is verified by comprehensive experiments on wind speed series and benchmark datasets. The corresponding results are summarized as follows: (1) data pretreatment technology based on TVFEMD can effectively reduce the non-stationarity of raw wind speed series; (2) the NNCT-based multi-model fusion strategy can make up the shortcoming that single forecasting model cannot achieve well prediction results for all decomposed subsequences; (3) the proposed GHWOADE algorithm is employed to optimize weight coefficients of corresponding forecasting models, which contributes to promoting wind speed prediction precision.