Abstract
Accurate wind speed prediction plays a crucial role on the routine operational management of wind farms. However, the irregular characteristics of wind speed time series makes it hard to predict accurately. This study develops a novel forecasting strategy for multi-step wind speed forecasting (WSF) and illustrates its effectiveness. During the WSF process, a two-stage signal decomposition method combining ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) is exploited to decompose the empirical wind speed data. The EEMD algorithm is firstly employed to disassemble wind speed data into several intrinsic mode function (IMFs) and one residual (Res). The highest frequency component, IMF1, obtained by EEMD is further disassembled into different modes by the VMD algorithm. Then, feature selection is applied to eliminate the illusive components in the input-matrix predetermined by partial autocorrelation function (PACF) and the parameters in the proposed wavelet neural network (WNN) model are optimized for improving the forecasting performance, which are realized by hybrid backtracking search optimization algorithm (HBSA) integrating binary-valued BSA (BBSA) with real-valued BSA (RBSA), simultaneously. Combinations of Morlet function and Mexican hat function by weighted coefficient are constructed as activation functions for WNN, namely DAWNN, to enhance its regression performance. In the end, the final WSF values are obtained by assembling the prediction results of each decomposed components. Two sets of actual wind speed data are applied to evaluate and analyze the proposed forecasting strategy. Forecasting results, comparisons, and analysis illustrate that the proposed EEMD/VMD-HSBA-DAWNN is an effective model when employed in multi-step WSF.
Highlights
Sustainability transitions are beneficial transformation processes that make social production and consumption sustainable through socio-technical systems
Forecasting results, comparisons, and analysis illustrate that the proposed ensemble empirical mode decomposition (EEMD)/variational mode decomposition (VMD)-HSBA-DAWNN is an effective model when employed in multi-step wind speed forecasting (WSF)
In the hybrid backtracking search optimization algorithm (HBSA)-DAWNN model, the parameter combination in DAWNN are tuned by real-valued BSA (RBSA) method and the effective input variables are selected by binary-valued BSA (BBSA) technique
Summary
Sustainability transitions are beneficial transformation processes that make social production and consumption sustainable through socio-technical systems. WNN tuned by genetic algorithm (GA) and variational mode decomposition (VMD) These hybrid forecasting models firstly utilize single signal decomposition technique to break the original empirical wind speed into different sub-series, the sub-series variables are employed as the inputs of AI methods for WSF. The previous literatures illustrate that AI models combined with multi-scale decomposition techniques achieve satisfactory forecasting results, single signal decomposition methods cannot often thoroughly tackle the non-stationary and non-linear components in the wind speed. Inspired by the forecasting mechanism in the previous literatures, a novel combined strategy using two-stage decomposition technique, hybrid backtracking search optimization algorithm, WNN with double activations through weighted coefficient (DAWNN), namely EEMD/VMD- HBSA-DAWNN, is proposed for short-term WSF. A two-stage decomposition technique combining EEMD with VMD is exploited to deal with wind speed data, eliminating the characteristic of irregularities. The abbreviations of technical terms are listed in Appendix A
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
