Predictive modelling and optimization of HVAC systems using neural network and particle swarm optimization algorithm

Afroz, Z; Shafiullah, GM; Urmee, T; Shoeb, MA; Higgins, G

Predictive modelling and optimization of HVAC systems using neural network and particle swarm optimization algorithm

Afroz, Z

Shafiullah, GM Urmee, T Shoeb, MA Higgins, G

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Building and Environment, 2022, 209
Issue Date:: 2022-02-01

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Field	Value	Language
dc.contributor.author	Afroz, Z https://orcid.org/0000-0002-4053-108X
dc.contributor.author	Shafiullah, GM
dc.contributor.author	Urmee, T
dc.contributor.author	Shoeb, MA
dc.contributor.author	Higgins, G
dc.date.accessioned	2022-12-14T23:22:40Z
dc.date.available	2022-12-14T23:22:40Z
dc.date.issued	2022-02-01
dc.identifier.citation	Building and Environment, 2022, 209
dc.identifier.issn	0360-1323
dc.identifier.issn	1873-684X
dc.identifier.uri	http://hdl.handle.net/10453/164420
dc.description.abstract	The concept of maintaining indoor environmental quality comprising building indoor temperature, relative humidity, CO2, and volatile organic compound (VOC) level poses new challenges to the optimal operation of heating, ventilation and air-conditioning (HVAC) systems. While existing case studies demonstrate the energy-saving potentials for efficient HVAC operation, there is a lack of studies quantifying energy savings whilst considering indoor environmental conditions. This study proposes a state-of-the-art modelling and optimization approach to minimize the energy consumption of the HVAC systems without compromising indoor environmental quality. While the primary objective of ensuring optimal operation of HVAC systems is to minimize energy consumption, controlling indoor environmental parameters to remain within the acceptable range imposes excess energy use. These two conflicting objectives constitute a multi-variable constrained optimization problem that has been solved using a particle swarm optimization (PSO) algorithm. Real-time predictive models are developed for the individual indoor environmental parameters and HVAC energy consumption using a Nonlinear Autoregressive Exogenous (NARX) neural network (NN). During model development, models' performance is optimized in terms of complexity, predictive accuracy, and ease of application to a real system. The proposed predictive models are then optimized to provide an optimal control setting for the HVAC systems considering seasonal variations. The results indicate that it is possible to reduce 7.8% of total energy, without negotiating indoor environmental conditions, e.g., air temperature 19.60–28.20°C and relative humidity 30–65% as per ASHRAE Standard 55, and CO2 ≤ 800 ppm and VOC ≤1000 ppm as per AS 1668.2.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Building and Environment
dc.relation.isbasedon	10.1016/j.buildenv.2021.108681
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0502 Environmental Science and Management, 1201 Architecture, 1202 Building
dc.subject.classification	Building & Construction
dc.title	Predictive modelling and optimization of HVAC systems using neural network and particle swarm optimization algorithm
dc.type	Journal Article
utslib.citation.volume	209
utslib.for	0502 Environmental Science and Management
utslib.for	1201 Architecture
utslib.for	1202 Building
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 Built Environment
utslib.copyright.status	closed_access	*
dc.date.updated	2022-12-14T23:22:16Z
pubs.publication-status	Published
pubs.volume	209

Abstract:

The concept of maintaining indoor environmental quality comprising building indoor temperature, relative humidity, CO2, and volatile organic compound (VOC) level poses new challenges to the optimal operation of heating, ventilation and air-conditioning (HVAC) systems. While existing case studies demonstrate the energy-saving potentials for efficient HVAC operation, there is a lack of studies quantifying energy savings whilst considering indoor environmental conditions. This study proposes a state-of-the-art modelling and optimization approach to minimize the energy consumption of the HVAC systems without compromising indoor environmental quality. While the primary objective of ensuring optimal operation of HVAC systems is to minimize energy consumption, controlling indoor environmental parameters to remain within the acceptable range imposes excess energy use. These two conflicting objectives constitute a multi-variable constrained optimization problem that has been solved using a particle swarm optimization (PSO) algorithm. Real-time predictive models are developed for the individual indoor environmental parameters and HVAC energy consumption using a Nonlinear Autoregressive Exogenous (NARX) neural network (NN). During model development, models' performance is optimized in terms of complexity, predictive accuracy, and ease of application to a real system. The proposed predictive models are then optimized to provide an optimal control setting for the HVAC systems considering seasonal variations. The results indicate that it is possible to reduce 7.8% of total energy, without negotiating indoor environmental conditions, e.g., air temperature 19.60–28.20°C and relative humidity 30–65% as per ASHRAE Standard 55, and CO2 ≤ 800 ppm and VOC ≤1000 ppm as per AS 1668.2.

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http://hdl.handle.net/10453/164420