A hybrid approach for multi-step wind speed forecasting based on two-layer decomposition, improved hybrid DE-HHO optimization and KELM

Fu, Wenlong; Zhang, Kai; Wang, Kai; Wen, Bangchun; Fang, Ping; Zou, Feng

doi:10.1016/j.renene.2020.09.078

Cited by 83 publications

(26 citation statements)

References 41 publications

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“…This article uses a novel natural technology called ALO. Mirjalili et al 50 first put forward ALO algorithm, which consists of two aggregates: ants and ant antlions 51 . In addition, the ALO algorithm has six main operations: random walk of ants, trap in antlion's pits, slide ants towards antlions, prey capture and nest reconstruction, and elitism 52 .…”

Section: The Forecasting Systemmentioning

confidence: 99%

Research of a combined wind speed model based on multi‐objective ant lion optimization algorithm

Wang

et al. 2021

Int Trans Electr Energ Syst

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In the past few decades, wind power generation has gradually become an important source in the global energy market, which effectively meets the energy demand of human production activities. However, the instability and diffusion of wind speed bring difficulties to the wind energy development and promotion. For improving the predictive accuracy of original sequence, scholars have proposed a variety of prediction models, but many current forecasting models often neglect the importance of data processing and can be easily limited by a single model, which causes poor performances. Therefore, a combined model is built that mainly includes the complete ensemble empirical mode decomposition with adaptive noise, several single models, and multiobjective ant lion optimization algorithm. This combined model not only reduces the impact of high-frequency noise, but also extracts original sequences features as much as possible, combines the advantages of multiple single models, and greatly improves forecasting accuracy and stability.

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Section: The Forecasting Systemmentioning

confidence: 99%

Research of a combined wind speed model based on multi‐objective ant lion optimization algorithm

Wang

et al. 2021

Int Trans Electr Energ Syst

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“…Besides traditional AI/ML models, deep learning and extreme learning machines are also commonly applied in wind speed forecasting. Notable architectures include Kernel Extreme Learning Machine (KELM) [13,14], Long Short-Term Memory (LSTM) [15,16], Echo State Network [17], Deep Belief Network (DBN) [18,19], and Convolutional Neural Network (CNN) [20,21].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Speed Forecasting Based on Hybrid MODWT-ARIMA-Markov Model

2021

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Markov chains (MC) are statistical models used to predict very short to short-term wind speed accurately. Such models are generally trained with a single moving window. However, wind speed time series do not possess an equal length of behavior for all horizons. Therefore, a single moving window can provide reasonable estimates but is not an optimal choice. In this study, a forecasting model is proposed that integrates MCs with an adjusting dynamic moving window. The model selects the optimal size of the window based on a similar approach to the leave-one-out method. The traditional model is further optimized by introducing a self-adaptive state categorization algorithm. Instead of synthetically generating time series, the modified model directly predicts one-step ahead wind speed. Initial results indicate that adjusting the moving window MC prediction model improved the forecasting performance of a single moving window approach by 50%. Based on preliminary findings, a novel hybrid model is proposed integrating maximal overlap discrete wavelet transform (MODWT) with auto-regressive integrated moving average (ARIMA) and adjusting moving window MC. It is evident from the literature that MC models are suitable for predicting residual sequences. However, MCs were not considered as a primary forecasting model for the decomposition-based hybrid approach in any wind forecasting studies. The improvement of the novel model is, on average, 55% for single deep learning models and 30% for decomposition-based hybrid models.

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“…The SVM model is easily affected by penalty factor C and kernel parameter g when performing pattern recognition. To address this issue, many optimization algorithms have been used to optimize the SVM model, for instance, Harris hawks optimization (HHO) [ 34 , 35 ], whale optimization algorithm (WOA) [ 36 ], particle swarm optimization (PSO) [ 37 ], moth−flame optimization (MFO) [ 38 ], differential evolution (DE) [ 39 ], sine cosine algorithm (SCA) [ 40 ] and grey wolf optimization (GWO) [ 41 ]. Although these intelligent optimization algorithms have achieved some favorable results, there are still problems of premature convergence of different degrees.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Shi

et al. 2021

Entropy

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Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.

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A hybrid approach for multi-step wind speed forecasting based on two-layer decomposition, improved hybrid DE-HHO optimization and KELM

Cited by 83 publications

References 41 publications

Research of a combined wind speed model based on multi‐objective ant lion optimization algorithm

Research of a combined wind speed model based on multi‐objective ant lion optimization algorithm

Short-Term Wind Speed Forecasting Based on Hybrid MODWT-ARIMA-Markov Model

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

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