Enhancing short-term wind speed prediction based on an outlier-robust ensemble deep random vector functional link network with AOA-optimized VMD

Wind speed prediction is a crucial aspect in the utilization of wind energy. In this paper, a wind speed prediction model based on an outlier-robust ensemble deep random vector functional link network (ORedRVFL) and arithmetic optimization algorithm-optimized variational mode decomposition (AOA-VMD)...

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Vydáno v:	Energy (Oxford) Ročník 296; s. 131173
Hlavní autoři:	Zhang, Chu, Li, Zhengbo, Ge, Yida, Liu, Qianlong, Suo, Leiming, Song, Shihao, Peng, Tian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.06.2024
Témata:	algorithms Anti-interference ability Arithmetic optimization algorithm arithmetics energy Ensemble deep random vector functional link network prediction wind power wind speed Wind speed prediction Anti-interference ability Arithmetic optimization algorithm Wind speed prediction Ensemble deep random vector functional link network
ISSN:	0360-5442
On-line přístup:	Získat plný text
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Shrnutí:	Wind speed prediction is a crucial aspect in the utilization of wind energy. In this paper, a wind speed prediction model based on an outlier-robust ensemble deep random vector functional link network (ORedRVFL) and arithmetic optimization algorithm-optimized variational mode decomposition (AOA-VMD) is designed. First, the penalty factor and the number of mode decompositions of VMD are optimized using the AOA algorithm and the original data are decomposed using the optimized VMD. Then the decomposed data is predicted using the ensemble deep random vector functional link network (edRVFL) model. The edRVFL uses rich intermediate features for the final decision, which can make the final result closer to the real data. In order to strengthen the anti-interference ability to the outliers, this paper robustly improves the edRVFL model, and the improved model is called ORedRVFL. ORedRVFL reduces the impact of outliers by introducing regularization and norm to balance the relationship between training error and weights. The experiments have proved that the model proposed in this paper outperforms other models in terms of anti-interference ability and prediction accuracy. •An outlier-robust edRVRFL prediction model is developed.•Use VMD to decompose the data and enhance the model prediction capability.•Fuzzy entropy is used as a criterion for data aggregation.•AOA optimizes the VMD hyperparameters to enhance the decomposition effect of VMD.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0360-5442
DOI:	10.1016/j.energy.2024.131173