Developing a Hybrid Model of Prediction and Classification Algorithms for Building Energy Consumption

Banihashemi, S; Ding, G; Wang, J

Developing a Hybrid Model of Prediction and Classification Algorithms for Building Energy Consumption

Banihashemi, S

Ding, G

Wang, J

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Publication Type:: Journal Article
Citation:: Energy Procedia, 2017, 110 pp. 371 - 376
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Banihashemi, S https://orcid.org/0000-0002-7438-1235	en_US
dc.contributor.author	Ding, G https://orcid.org/0000-0003-2697-9395	en_US
dc.contributor.author	Wang, J https://orcid.org/0000-0002-6282-0456	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Energy Procedia, 2017, 110 pp. 371 - 376	en_US
dc.identifier.issn	1876-6102	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127826
dc.description.abstract	© 2017 The Authors. Artificial intelligence algorithms have been applied separately or integrally for prediction, classification or optimization of buildings energy consumption. However, there is a salient gap in the literature on the investigation of hybrid objective function development for energy optimization problems including qualitative and quantitative datasets in their constructs. To tackle with this challenge, this paper presents a hybrid objective function of machine learning algorithms in optimizing energy consumption of residential buildings through considering both continuous and discrete parameters of energy simultaneously. To do this, a comprehensive dataset including significant parameters of building envelop, building design layout and HVAC was established, Artificial Neural Network as a prediction and Decision Tree as a classification algorithm were employed via cross-training ensemble equation to create the hybrid function and the model was finally validated via the weighted average of the error decomposed for the performance. The developed model could effectively enhance the accuracy of the objective functions used in the building energy prediction and optimization problems. Furthermore, the results of this novel approach resolved the inclusion issue of both continuous and discrete parameters of energy in a unified objective function without threatening the integrity and consistency of the building energy datasets.	en_US
dc.relation.ispartof	Energy Procedia	en_US
dc.relation.isbasedon	10.1016/j.egypro.2017.03.155	en_US
dc.title	Developing a Hybrid Model of Prediction and Classification Algorithms for Building Energy Consumption	en_US
dc.type	Journal Article
utslib.citation.volume	110	en_US
utslib.for	0905 Civil Engineering	en_US
utslib.for	120201 Building Construction Management and Project Planning	en_US
utslib.for	1202 Building	en_US
utslib.for	090502 Construction Engineering	en_US
utslib.for	0904 Chemical Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/e-Press
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Built Environment
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 Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
pubs.organisational-group	/University of Technology Sydney/Strength - CCDP - Contemporary Design Practice
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	110	en_US

Abstract:

© 2017 The Authors. Artificial intelligence algorithms have been applied separately or integrally for prediction, classification or optimization of buildings energy consumption. However, there is a salient gap in the literature on the investigation of hybrid objective function development for energy optimization problems including qualitative and quantitative datasets in their constructs. To tackle with this challenge, this paper presents a hybrid objective function of machine learning algorithms in optimizing energy consumption of residential buildings through considering both continuous and discrete parameters of energy simultaneously. To do this, a comprehensive dataset including significant parameters of building envelop, building design layout and HVAC was established, Artificial Neural Network as a prediction and Decision Tree as a classification algorithm were employed via cross-training ensemble equation to create the hybrid function and the model was finally validated via the weighted average of the error decomposed for the performance. The developed model could effectively enhance the accuracy of the objective functions used in the building energy prediction and optimization problems. Furthermore, the results of this novel approach resolved the inclusion issue of both continuous and discrete parameters of energy in a unified objective function without threatening the integrity and consistency of the building energy datasets.

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

http://hdl.handle.net/10453/126010