Detecting Patterns in Energy Use and Greenhouse Gas Emissions of Cities Using Machine Learning

Cities are expected to play a major role in managing climate change in the coming decades. The actual environmental performance of urban centres is difficult to predict due to the complex interplay of technologies and infrastructure with social, economic, and political factors. Machine learning (ML)...

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Autores principales:	Kathleen B. Aviso, Marc Joseph Capili, Hon Huin Chin, Yee Van Fan, Jirí Jaromír Klemeš, Raymond R. Tan
Formato:	article
Lenguaje:	EN
Publicado:	AIDIC Servizi S.r.l. 2021
Materias:	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895
Acceso en línea:	https://doaj.org/article/ffabfbf8f0bc43bb85f16eb1d471f469
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id	oai:doaj.org-article:ffabfbf8f0bc43bb85f16eb1d471f469
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spelling	oai:doaj.org-article:ffabfbf8f0bc43bb85f16eb1d471f4692021-11-15T21:48:25ZDetecting Patterns in Energy Use and Greenhouse Gas Emissions of Cities Using Machine Learning10.3303/CET21880672283-9216https://doaj.org/article/ffabfbf8f0bc43bb85f16eb1d471f4692021-11-01T00:00:00Zhttps://www.cetjournal.it/index.php/cet/article/view/11860https://doaj.org/toc/2283-9216Cities are expected to play a major role in managing climate change in the coming decades. The actual environmental performance of urban centres is difficult to predict due to the complex interplay of technologies and infrastructure with social, economic, and political factors. Machine learning (ML) techniques can be used to detect patterns in high-level city data to determine factors that influence favourable climate performance. In this work, rough set-based ML (RSML) is used to identify such patterns in the Sustainable Cities Index (SCI), which ranks 100 of the world’s major urban centres based on three broad criteria that cover social, environmental, and economic dimensions. These main criteria are further broken down into 18 detailed criteria that are used to calculate the aggregate SCI scores of the listed cities. Two of the environmental criteria measure energy intensity and greenhouse gas (GHG) emissions. RSML is used to generate interpretable rule-based (if/then) models that predict energy utilisation and GHG emissions performance of cities based on the other criteria in the database. Attribute reduction techniques are used to identify a set of 7 non-redundant criteria for energy use and 9 non-redundant criteria for GHG emissions; 6 criteria are common to these two sets. Then, RSML is used to generate rule-based models. A 10-rule model is determined for energy intensity, while an 11-rule model is found for GHG emissions. Both models were reduced further by eliminating rules with weak generalisation capability. A key insight from the rule-based models is that social, environmental, and economic attributes are associated with energy intensity and GHG emissions due to indirect effects.Kathleen B. AvisoMarc Joseph CapiliHon Huin ChinYee Van FanJirí Jaromír KlemešRaymond R. TanAIDIC Servizi S.r.l.articleChemical engineeringTP155-156Computer engineering. Computer hardwareTK7885-7895ENChemical Engineering Transactions, Vol 88 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895
spellingShingle	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895 Kathleen B. Aviso Marc Joseph Capili Hon Huin Chin Yee Van Fan Jirí Jaromír Klemeš Raymond R. Tan Detecting Patterns in Energy Use and Greenhouse Gas Emissions of Cities Using Machine Learning
description	Cities are expected to play a major role in managing climate change in the coming decades. The actual environmental performance of urban centres is difficult to predict due to the complex interplay of technologies and infrastructure with social, economic, and political factors. Machine learning (ML) techniques can be used to detect patterns in high-level city data to determine factors that influence favourable climate performance. In this work, rough set-based ML (RSML) is used to identify such patterns in the Sustainable Cities Index (SCI), which ranks 100 of the world’s major urban centres based on three broad criteria that cover social, environmental, and economic dimensions. These main criteria are further broken down into 18 detailed criteria that are used to calculate the aggregate SCI scores of the listed cities. Two of the environmental criteria measure energy intensity and greenhouse gas (GHG) emissions. RSML is used to generate interpretable rule-based (if/then) models that predict energy utilisation and GHG emissions performance of cities based on the other criteria in the database. Attribute reduction techniques are used to identify a set of 7 non-redundant criteria for energy use and 9 non-redundant criteria for GHG emissions; 6 criteria are common to these two sets. Then, RSML is used to generate rule-based models. A 10-rule model is determined for energy intensity, while an 11-rule model is found for GHG emissions. Both models were reduced further by eliminating rules with weak generalisation capability. A key insight from the rule-based models is that social, environmental, and economic attributes are associated with energy intensity and GHG emissions due to indirect effects.
format	article
author	Kathleen B. Aviso Marc Joseph Capili Hon Huin Chin Yee Van Fan Jirí Jaromír Klemeš Raymond R. Tan
author_facet	Kathleen B. Aviso Marc Joseph Capili Hon Huin Chin Yee Van Fan Jirí Jaromír Klemeš Raymond R. Tan
author_sort	Kathleen B. Aviso
title	Detecting Patterns in Energy Use and Greenhouse Gas Emissions of Cities Using Machine Learning
title_short	Detecting Patterns in Energy Use and Greenhouse Gas Emissions of Cities Using Machine Learning
title_full	Detecting Patterns in Energy Use and Greenhouse Gas Emissions of Cities Using Machine Learning
title_fullStr	Detecting Patterns in Energy Use and Greenhouse Gas Emissions of Cities Using Machine Learning
title_full_unstemmed	Detecting Patterns in Energy Use and Greenhouse Gas Emissions of Cities Using Machine Learning
title_sort	detecting patterns in energy use and greenhouse gas emissions of cities using machine learning
publisher	AIDIC Servizi S.r.l.
publishDate	2021
url	https://doaj.org/article/ffabfbf8f0bc43bb85f16eb1d471f469
work_keys_str_mv	AT kathleenbaviso detectingpatternsinenergyuseandgreenhousegasemissionsofcitiesusingmachinelearning AT marcjosephcapili detectingpatternsinenergyuseandgreenhousegasemissionsofcitiesusingmachinelearning AT honhuinchin detectingpatternsinenergyuseandgreenhousegasemissionsofcitiesusingmachinelearning AT yeevanfan detectingpatternsinenergyuseandgreenhousegasemissionsofcitiesusingmachinelearning AT jirijaromirklemes detectingpatternsinenergyuseandgreenhousegasemissionsofcitiesusingmachinelearning AT raymondrtan detectingpatternsinenergyuseandgreenhousegasemissionsofcitiesusingmachinelearning
_version_	1718426824035270656

Detecting Patterns in Energy Use and Greenhouse Gas Emissions of Cities Using Machine Learning

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