Short-term photovoltaic power forecasting based on signal decomposition and machine learning optimization

Zhou, Y; Wang, J; Li, Z; Lu, H

Short-term photovoltaic power forecasting based on signal decomposition and machine learning optimization

Zhou, Y Wang, J Li, Z Lu, H

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Energy Conversion and Management, 2022, 267
Issue Date:: 2022-09-01

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Field	Value	Language
dc.contributor.author	Zhou, Y
dc.contributor.author	Wang, J
dc.contributor.author	Li, Z
dc.contributor.author	Lu, H https://orcid.org/0000-0001-5655-0237
dc.date.accessioned	2023-02-07T03:01:57Z
dc.date.available	2023-02-07T03:01:57Z
dc.date.issued	2022-09-01
dc.identifier.citation	Energy Conversion and Management, 2022, 267
dc.identifier.issn	0196-8904
dc.identifier.issn	1879-2227
dc.identifier.uri	http://hdl.handle.net/10453/165965
dc.description.abstract	Owing to the continuous increase in the proportion of solar generation accounting for the total global generation, real-time management of solar power has become indispensable. Moreover, accurate prediction of photovoltaic power is emerging as an important link to support grid operations and reflect real-life scenarios. Various studies have led to the design of several forecasting models. Nevertheless, most predictors do not focus on the effects of the factors of photovoltaic modules on the forecast results. To fill this gap, in this paper, a novel multivariable hybrid prediction system combining signal decomposition, artificial intelligence models, deep learning models, and a swarm intelligence optimization strategy is proposed. This system fully utilizes independent variable features (including the module temperature) to efficiently enhance the precision and efficiency of photovoltaic forecasting. In particular, it is proved that a Pareto-optimal solution can be obtained using the designed system. Using three datasets obtained from Safi-Morocco, the presented system is verified by comparative experiments, and its remarkable advantages in terms of forecasting are demonstrated. Specifically, using the three datasets, the symmetric mean absolute percentage errors obtained by the presented forecast system are 2.129%, 2.335%, and 3.654%, respectively, which are significantly lower than those achieved with other comparison models. Furthermore, a comprehensive and rational evaluation methodology is employed to assess the predictive capability of the developed system. The evaluation results show that the system is effective in improving the forecasting efficiency and outperforms other benchmark models.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Energy Conversion and Management
dc.relation.isbasedon	10.1016/j.enconman.2022.115944
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Energy
dc.title	Short-term photovoltaic power forecasting based on signal decomposition and machine learning optimization
dc.type	Journal Article
utslib.citation.volume	267
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical 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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2024-09-01T00:00:00+1000Z
dc.date.updated	2023-02-07T03:00:08Z
pubs.publication-status	Published
pubs.volume	267

Abstract:

Owing to the continuous increase in the proportion of solar generation accounting for the total global generation, real-time management of solar power has become indispensable. Moreover, accurate prediction of photovoltaic power is emerging as an important link to support grid operations and reflect real-life scenarios. Various studies have led to the design of several forecasting models. Nevertheless, most predictors do not focus on the effects of the factors of photovoltaic modules on the forecast results. To fill this gap, in this paper, a novel multivariable hybrid prediction system combining signal decomposition, artificial intelligence models, deep learning models, and a swarm intelligence optimization strategy is proposed. This system fully utilizes independent variable features (including the module temperature) to efficiently enhance the precision and efficiency of photovoltaic forecasting. In particular, it is proved that a Pareto-optimal solution can be obtained using the designed system. Using three datasets obtained from Safi-Morocco, the presented system is verified by comparative experiments, and its remarkable advantages in terms of forecasting are demonstrated. Specifically, using the three datasets, the symmetric mean absolute percentage errors obtained by the presented forecast system are 2.129%, 2.335%, and 3.654%, respectively, which are significantly lower than those achieved with other comparison models. Furthermore, a comprehensive and rational evaluation methodology is employed to assess the predictive capability of the developed system. The evaluation results show that the system is effective in improving the forecasting efficiency and outperforms other benchmark models.

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http://hdl.handle.net/10453/165965