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A systematic review of multi-output prediction model for indoor environment and heating, ventilation, and air conditioning energy consumption in buildings

Heating, ventilation and air conditioning (HVAC) systems could significantly impact indoor environmental quality, particularly in terms of thermal comfort and indoor air quality. Achieving a high-quality indoor environment poses challenges to the energy consumption of HVAC systems. Thus, balancing t...

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Main Authors:	Jiang, Kaiyun, Shi, Tianyu, Yu, Haowei, Mahyuddin, Norhayati, Lu, Shifeng
Format:	Article
Published:	SAGE Publications 2024
Subjects:	TD Environmental technology. Sanitary engineering TH Building construction
Online Access:	http://eprints.um.edu.my/46888/ https://doi.org/10.1177/1420326X241258678
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id	my.um.eprints.46888
record_format	eprints
spelling	my.um.eprints.468882025-01-16T01:46:19Z http://eprints.um.edu.my/46888/ A systematic review of multi-output prediction model for indoor environment and heating, ventilation, and air conditioning energy consumption in buildings Jiang, Kaiyun Shi, Tianyu Yu, Haowei Mahyuddin, Norhayati Lu, Shifeng TD Environmental technology. Sanitary engineering TH Building construction Heating, ventilation and air conditioning (HVAC) systems could significantly impact indoor environmental quality, particularly in terms of thermal comfort and indoor air quality. Achieving a high-quality indoor environment poses challenges to the energy consumption of HVAC systems. Thus, balancing thermal comfort, indoor air quality (IAQ) and energy consumption becomes a challenging task. Currently, indoor environment prediction methods are considered effective solutions to address this issue. However, the published literature usually concentrates on single aspects like thermal comfort, air quality or energy consumption, with multi-aspect prediction methods being rare. The present work reviews research spanning the last decade that employs machine learning methods for predicting indoor environments and HVAC energy consumption through separate and multi-output predictive models. Separate predictive models focus on HVAC systems' impact on the indoor environment, while multi-output models consider the interplay of various outputs. This article gives a thorough insight into machine learning prediction models' workflow, detailing data collection, feature selection and model optimization for each research goal. A systematic assessment of methods for data collection of diverse prediction targets, machine learning algorithms and validation approaches for different prediction models is presented. This review highlights the complexities of data management, model development and validation, enriching the knowledge base in indoor environmental quality optimization. SAGE Publications 2024-11 Article PeerReviewed Jiang, Kaiyun and Shi, Tianyu and Yu, Haowei and Mahyuddin, Norhayati and Lu, Shifeng (2024) A systematic review of multi-output prediction model for indoor environment and heating, ventilation, and air conditioning energy consumption in buildings. Indoor and Built Environment, 33 (9). pp. 1574-1604. ISSN 1420-326X, DOI https://doi.org/10.1177/1420326X241258678 <https://doi.org/10.1177/1420326X241258678>. https://doi.org/10.1177/1420326X241258678 10.1177/1420326X241258678
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	TD Environmental technology. Sanitary engineering TH Building construction
spellingShingle	TD Environmental technology. Sanitary engineering TH Building construction Jiang, Kaiyun Shi, Tianyu Yu, Haowei Mahyuddin, Norhayati Lu, Shifeng A systematic review of multi-output prediction model for indoor environment and heating, ventilation, and air conditioning energy consumption in buildings
description	Heating, ventilation and air conditioning (HVAC) systems could significantly impact indoor environmental quality, particularly in terms of thermal comfort and indoor air quality. Achieving a high-quality indoor environment poses challenges to the energy consumption of HVAC systems. Thus, balancing thermal comfort, indoor air quality (IAQ) and energy consumption becomes a challenging task. Currently, indoor environment prediction methods are considered effective solutions to address this issue. However, the published literature usually concentrates on single aspects like thermal comfort, air quality or energy consumption, with multi-aspect prediction methods being rare. The present work reviews research spanning the last decade that employs machine learning methods for predicting indoor environments and HVAC energy consumption through separate and multi-output predictive models. Separate predictive models focus on HVAC systems' impact on the indoor environment, while multi-output models consider the interplay of various outputs. This article gives a thorough insight into machine learning prediction models' workflow, detailing data collection, feature selection and model optimization for each research goal. A systematic assessment of methods for data collection of diverse prediction targets, machine learning algorithms and validation approaches for different prediction models is presented. This review highlights the complexities of data management, model development and validation, enriching the knowledge base in indoor environmental quality optimization.
format	Article
author	Jiang, Kaiyun Shi, Tianyu Yu, Haowei Mahyuddin, Norhayati Lu, Shifeng
author_facet	Jiang, Kaiyun Shi, Tianyu Yu, Haowei Mahyuddin, Norhayati Lu, Shifeng
author_sort	Jiang, Kaiyun
title	A systematic review of multi-output prediction model for indoor environment and heating, ventilation, and air conditioning energy consumption in buildings
title_short	A systematic review of multi-output prediction model for indoor environment and heating, ventilation, and air conditioning energy consumption in buildings
title_full	A systematic review of multi-output prediction model for indoor environment and heating, ventilation, and air conditioning energy consumption in buildings
title_fullStr	A systematic review of multi-output prediction model for indoor environment and heating, ventilation, and air conditioning energy consumption in buildings
title_full_unstemmed	A systematic review of multi-output prediction model for indoor environment and heating, ventilation, and air conditioning energy consumption in buildings
title_sort	systematic review of multi-output prediction model for indoor environment and heating, ventilation, and air conditioning energy consumption in buildings
publisher	SAGE Publications
publishDate	2024
url	http://eprints.um.edu.my/46888/ https://doi.org/10.1177/1420326X241258678
_version_	1825160604421193728
score	13.239859

A systematic review of multi-output prediction model for indoor environment and heating, ventilation, and air conditioning energy consumption in buildings

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