A New Wind Speed Forecasting Modeling Strategy Using Two-Stage Decomposition, Feature Selection and DAWNN

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.

Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

“…Considering the feature selection, signal decomposition, and parameter optimisation, a variant of the DAWNN method tuned by the hybrid BSA and merged with two-state decomposition EEMD/VMD was suggested for the modelling strategy to forecast wind speed [92]. The proposed method is called EEMD/VMD-HBSA-DAWNN.…”

The Green Photovoltaic Industry Installed Capacity Forecast in China: Based on Grey Relation Analysis, Improved Signal Decomposition Method, and Artificial Bee Colony Algorithm

“…PV installed capacity prediction is a complex nonlinear solution problem. So far, many scholars have proposed various prediction models, such as grey theory [10,11], multiple regression [12,13], and time series [14,15]. In recent years, various intelligent algorithms and signal decomposition models have also been widely used in installed capacity prediction [16][17][18][19][20][21][22][23][24][25].…”

“…According to the government and nongovernment agencies that have planned photovoltaic projects, it is predicted that future PV systems will generate 0.8 TWh of electricity to meet the power demands of 2027. Sun et al [14] proposed a new predictive model for multistep wind speed prediction (WSF). A twostage signal decomposition method combining set empirical mode decomposition (EEMD) and variational mode decomposition (VMD) was used to decompose empirical wind speed data.…”

A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

“…Wavelet neural network is constructed based on feedforward back-propagation network (BPNN), namely, the transfer function, also named activation function, in hidden neurons nodes of BPNN is replaced by wavelet function [32][33][34] . The basic structure of WNN includes an input layer, hidden layer, and output layer.…”