Wind speed prediction by a swarm intelligence based deep learning model via signal decomposition and parameter optimization using improved chimp optimization algorithm

Accurate prediction of wind speed plays a very important role in the stable operation of wind power plants. In this study, the goal is to establish a hybrid wind speed prediction model based on Time Varying Filtering based Empirical Mode Decomposition (TVFEMD), Fuzzy Entropy (FE), Partial Autocorrel...

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Veröffentlicht in:Energy (Oxford) Jg. 276; S. 127526
Hauptverfasser: Suo, Leiming, Peng, Tian, Song, Shihao, Zhang, Chu, Wang, Yuhan, Fu, Yongyan, Nazir, Muhammad Shahzad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.08.2023
Schlagworte:
algorithms
autocorrelation
BiGRU
Chimp optimization algorithm
Deep learning
energy
entropy
prediction
swarms
TVFEMD
wind power
wind speed
Wind speed prediction
Deep learning
BiGRU
Chimp optimization algorithm
Wind speed prediction
TVFEMD
ISSN:0360-5442
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Zusammenfassung:Accurate prediction of wind speed plays a very important role in the stable operation of wind power plants. In this study, the goal is to establish a hybrid wind speed prediction model based on Time Varying Filtering based Empirical Mode Decomposition (TVFEMD), Fuzzy Entropy (FE), Partial Autocorrelation Function (PACF), improved Chimp Optimization Algorithm (IChOA) and Bi-directional Gated Recurrent Unit (BiGRU). Firstly, the original wind speed data was decomposed by TVFEMD to obtain modal components, and FE aggregation is used to decrease the computational complexity. Secondly, the components are processed by PACF to extract important input features. Thirdly, the BiGRU parameters are optimized using IChOA which is an improved version of ChOA. Finally, the optimized BiGRU is used to predict the decomposed components, and the predicted components are summed to obtain the final prediction result. In this experiment, the proposed model is used to predict the data of four months of a year from Station 46,060 of National Data Buoy Center, and the performance of eight benchmark models is analyzed. Experimental results show that TVFEMD and PACF can improve the prediction accuracy of the model. IChOA is feasible to optimize the parameters of BiGRU and can improve the prediction performance. •The original wind speed data was decomposed by TVFEMD to obtain IMFs and RES.•IMFs and RES are feature extracted using PACF to improve information utilization.•ChOA is improved by chaotic initialization and ranking mechanism optimization.•IChOA is adopted to optimize the parameters of BiGRU and predict the outcome.•The proposed model can predict the wind speed time series.
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ISSN:0360-5442
DOI:10.1016/j.energy.2023.127526