A data driven framework for modelling community energy demand

Patidar, S; Jenkins, DP; Peacock, AD; Debnath, KB

A data driven framework for modelling community energy demand

Patidar, S Jenkins, DP Peacock, AD Debnath, KB

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Publisher:: International Building Performance Simulation Association
Publication Type:: Conference Proceeding
Citation:: Building Simulation Conference Proceedings, 2023, 18, pp. 3539-3546
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Patidar, S
dc.contributor.author	Jenkins, DP
dc.contributor.author	Peacock, AD
dc.contributor.author	Debnath, KB
dc.date.accessioned	2024-05-30T20:14:07Z
dc.date.available	2024-05-30T20:14:07Z
dc.date.issued	2023-01-01
dc.identifier.citation	Building Simulation Conference Proceedings, 2023, 18, pp. 3539-3546
dc.identifier.issn	2522-2708
dc.identifier.uri	http://hdl.handle.net/10453/179354
dc.description.abstract	Data driven models that integrate advanced analytics involving statistical and machine learning algorithms are widely applied for simulating and predicting energy demand at the community level. These models are used to inform various energy efficiency measures, infrastructure development, planning and investment decision. The paper presents an innovative framework for simulating and projecting climate change impacts on the future dynamics of community energy demand. The modelling framework selectively couples some of the most advanced analytical approaches and its potential are demonstrated using a case study community “Auroville” located in India.
dc.language	en
dc.publisher	International Building Performance Simulation Association
dc.relation.ispartof	Building Simulation Conference Proceedings
dc.relation.ispartof	Proceedings of Building Simulation 2023: 18th Conference of IBPSA
dc.relation.isbasedon	10.26868/25222708.2023.1673
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A data driven framework for modelling community energy demand
dc.type	Conference Proceeding
utslib.citation.volume	18
pubs.organisational-group	University of Technology Sydney
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 Architecture
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-30T20:14:06Z
pubs.publication-status	Published
pubs.volume	18

Abstract:

Data driven models that integrate advanced analytics involving statistical and machine learning algorithms are widely applied for simulating and predicting energy demand at the community level. These models are used to inform various energy efficiency measures, infrastructure development, planning and investment decision. The paper presents an innovative framework for simulating and projecting climate change impacts on the future dynamics of community energy demand. The modelling framework selectively couples some of the most advanced analytical approaches and its potential are demonstrated using a case study community “Auroville” located in India.

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http://hdl.handle.net/10453/179354