Research and application of a combined model based on frequent pattern growth algorithm and multi-objective optimization for solar radiation forecasting

Heng, J; Wang, J; Xiao, L; Lu, H

Research and application of a combined model based on frequent pattern growth algorithm and multi-objective optimization for solar radiation forecasting

Heng, J Wang, J Xiao, L Lu, H

Permalink

Publication Type:: Journal Article
Citation:: Applied Energy, 2017, 208 pp. 845 - 866
Issue Date:: 2017-12-15

Closed Access

	Filename	Description	Size
	Research and application of a combined model based on frequent pattern.pdf	Published Version	2.59 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Heng, J	en_US
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Xiao, L	en_US
dc.contributor.author	Lu, H https://orcid.org/0000-0001-5655-0237	en_US
dc.date.issued	2017-12-15	en_US
dc.identifier.citation	Applied Energy, 2017, 208 pp. 845 - 866	en_US
dc.identifier.issn	0306-2619	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125347
dc.description.abstract	© 2017 Elsevier Ltd Solar radiation forecasting plays a significant role in precisely designing solar energy systems and in the efficient management of solar energy plants. Most research only focuses on accuracy improvements; however, for an effective forecasting model, considering only accuracy or stability is inadequate. To solve this problem, a combined model based on nondominated sorting-based multiobjective bat algorithm (NSMOBA) is developed for the optimization of weight coefficients of each model to achieve high accuracy and stability results simultaneously. In addition, a statistical method and data mining-based approach are used to determine the input variables for constructing the combined model. Monthly average solar radiation and meteorological variables from six datasets in the U.S. collected for case studies were used to assess the comprehensive performance (both in accuracy and stability) of the proposed combined model. The simulation in four experiments demonstrated the following: (a) the proposed combined model is suitable for providing accurate and stable solar radiation forecasting; (b) the combined model exhibits a more competitive forecasting performance than the individual models by using the advantage of each model; (c) the NSMOBA is an efficient algorithm for providing accurate forecasting results and improving the stability where the single bat algorithm is insufficient.	en_US
dc.relation.ispartof	Applied Energy	en_US
dc.relation.isbasedon	10.1016/j.apenergy.2017.09.063	en_US
dc.subject.classification	Energy	en_US
dc.title	Research and application of a combined model based on frequent pattern growth algorithm and multi-objective optimization for solar radiation forecasting	en_US
dc.type	Journal Article
utslib.citation.volume	208	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0103 Numerical and Computational Mathematics	en_US
utslib.for	09 Engineering	en_US
utslib.for	14 Economics	en_US
pubs.embargo.period	Not known	en_US
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
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	208	en_US

Abstract:

© 2017 Elsevier Ltd Solar radiation forecasting plays a significant role in precisely designing solar energy systems and in the efficient management of solar energy plants. Most research only focuses on accuracy improvements; however, for an effective forecasting model, considering only accuracy or stability is inadequate. To solve this problem, a combined model based on nondominated sorting-based multiobjective bat algorithm (NSMOBA) is developed for the optimization of weight coefficients of each model to achieve high accuracy and stability results simultaneously. In addition, a statistical method and data mining-based approach are used to determine the input variables for constructing the combined model. Monthly average solar radiation and meteorological variables from six datasets in the U.S. collected for case studies were used to assess the comprehensive performance (both in accuracy and stability) of the proposed combined model. The simulation in four experiments demonstrated the following: (a) the proposed combined model is suitable for providing accurate and stable solar radiation forecasting; (b) the combined model exhibits a more competitive forecasting performance than the individual models by using the advantage of each model; (c) the NSMOBA is an efficient algorithm for providing accurate forecasting results and improving the stability where the single bat algorithm is insufficient.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/125347