A wind speed forecasting model based on multi-objective algorithm and interpretability learning

Li, M; Yang, Y; He, Z; Guo, X; Zhang, R; Huang, B

A wind speed forecasting model based on multi-objective algorithm and interpretability learning

Li, M Yang, Y He, Z Guo, X Zhang, R Huang, B

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Energy, 2023, 269, pp. 126778
Issue Date:: 2023-04-15

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Field	Value	Language
dc.contributor.author	Li, M
dc.contributor.author	Yang, Y
dc.contributor.author	He, Z
dc.contributor.author	Guo, X
dc.contributor.author	Zhang, R
dc.contributor.author	Huang, B
dc.date.accessioned	2024-04-14T21:07:55Z
dc.date.available	2024-04-14T21:07:55Z
dc.date.issued	2023-04-15
dc.identifier.citation	Energy, 2023, 269, pp. 126778
dc.identifier.issn	0360-5442
dc.identifier.uri	http://hdl.handle.net/10453/177903
dc.description.abstract	More accurate and reliable wind speed forecasting results can provide an effective assessment of wind energy resources and improve the efficiency of wind energy utilization. Therefore, this paper is devoted to constructing accurate wind speed forecasting models and quantitatively analyzing the models by using interpretable analysis. Firstly, a multivariate wind speed forecasting model (PMI-CMOGSA-RELM) is proposed based on machine learning methods and a clustering-based multi-objective gravity search algorithm (CMOGSA). Among them, wavelet packet decomposition (WPD) is used for noise reduction, candidate input feature pool and partial mutual information (PMI) are used for feature selection, and CMOGSA is used to optimize the final combined weights. Secondly, a new evaluation metric, Absolute Error Coverage Probability (AECP), is proposed to better evaluate forecasting accuracy. Then, post-hoc attribution analysis methods and visualization tools are used to analyze the forecasting model interpretability to help us better analyze the robustness and reliability of the forecasted results. Finally, this paper validates and evaluates the proposed model with two data sets of different resolutions. The experimental results not only prove the rationality of the AECP evaluation criteria, but also demonstrate that the proposed model has smaller errors, higher estimation accuracy, and is easier to understand.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Energy
dc.relation.isbasedon	10.1016/j.energy.2023.126778
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0913 Mechanical Engineering, 0914 Resources Engineering and Extractive Metallurgy, 0915 Interdisciplinary Engineering
dc.subject.classification	Energy
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4012 Fluid mechanics and thermal engineering
dc.subject.classification	4017 Mechanical engineering
dc.title	A wind speed forecasting model based on multi-objective algorithm and interpretability learning
dc.type	Journal Article
utslib.citation.volume	269
utslib.for	0913 Mechanical Engineering
utslib.for	0914 Resources Engineering and Extractive Metallurgy
utslib.for	0915 Interdisciplinary Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-14T21:07:43Z
pubs.publication-status	Published
pubs.volume	269

Abstract:

More accurate and reliable wind speed forecasting results can provide an effective assessment of wind energy resources and improve the efficiency of wind energy utilization. Therefore, this paper is devoted to constructing accurate wind speed forecasting models and quantitatively analyzing the models by using interpretable analysis. Firstly, a multivariate wind speed forecasting model (PMI-CMOGSA-RELM) is proposed based on machine learning methods and a clustering-based multi-objective gravity search algorithm (CMOGSA). Among them, wavelet packet decomposition (WPD) is used for noise reduction, candidate input feature pool and partial mutual information (PMI) are used for feature selection, and CMOGSA is used to optimize the final combined weights. Secondly, a new evaluation metric, Absolute Error Coverage Probability (AECP), is proposed to better evaluate forecasting accuracy. Then, post-hoc attribution analysis methods and visualization tools are used to analyze the forecasting model interpretability to help us better analyze the robustness and reliability of the forecasted results. Finally, this paper validates and evaluates the proposed model with two data sets of different resolutions. The experimental results not only prove the rationality of the AECP evaluation criteria, but also demonstrate that the proposed model has smaller errors, higher estimation accuracy, and is easier to understand.

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http://hdl.handle.net/10453/177903