Machine Learning and Deep Learning Methods for Enhancing Building Energy Efficiency and Indoor Environmental Quality – A Review

Tien, Paige Wenbin; Wei, Shuangyu; Darkwa, Jo; Wood, Christopher J.; Calautit, John Kaiser

doi:10.1016/j.egyai.2022.100198

Cited by 110 publications

(51 citation statements)

References 122 publications

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“…The study found that ML and DL have been successfully applied in assessing the energy efficiency of buildings. (Tien et al, 2022)…”

Section: And DL Methods Of Improving Energy Efficiencymentioning

confidence: 99%

“…(Sharifi, 2019) (2020) and O'Dwyer et al ( 2020) discussed the impact of AI applications on energy combustion and management, while Perera et al (2021) researched co-optimization of energy systems in cities. As for building environments, Alammar et al (2021), Forouzandeh et al (2022), and Tien et al (2022 outlined AI applications' contribution to energy efficiency.…”

Section: (Sharifi and Yamagata 2014a)mentioning

confidence: 99%

“…The Fifth Assessment Report (AR5) of the UN Intergovernmental Panel on Climate Change has defined an energy system as "all components related to the production, conversion, delivery, and use of energy" (Tien et al, 2022), while the International Energy Agency has defined energy system resilience as "the capacity of the energy system and its components to cope with a hazardous event or trend, to respond in ways that maintain its essential functions, identity and structure as well as its capacity for adaptation, learning, and transformation" (Jasiūnas et al, 2021;Molyneaux et al, 2016;. Additionally, a resilient energy system can rapidly recover from vulnerabilities or disruptions, while continuing to provide affordable energy services (Erker et al, 2017;Jasiūnas et al, 2021;Sharifi, 2016).…”

Section: And Structural Resiliencementioning

confidence: 99%

“…Evolution of AI and energy system models. Source: The authors' insights, based on reviewing (Alzghoul et al, 2014;Bibri, 2021a;Bibri and Krogstie, 2019;Dey et al, 2020;Forootan et al, 2022;Mosavi et al, 2019;Seneviratne et al, 2022;Thomas et al, 2021; between cooling demand and related variables (e.g., outdoor temperature and occupancy activities), by using mapping functions from a dataset (Tien et al, 2022).…”

Section: Typical ML and Uer Workflowmentioning

confidence: 99%

“…In this context, the concepts of resilience in general, and UER in particular, have been applied to effectively prepare for, combat, absorb, adapt to, and recover from adverse disruptions (Eslamlou et al, 2022;Francis and Bekera, 2014;Gültekin, 2021;Masnavi et al, 2018;Hunter, 2021;Kapucu et al, 2021;Olazabal et al, 2012;Tumini et al, 2017). Tien et al (2022) considered the resilience-focused approach to energy systems to be multifaceted, having found that many studies only focused on energy supply network faults on a spatial scale larger than a single city, while ignoring the complications specific to urban environments. In this context, Erker et al (2017) demonstrated that an energy-resilient system can successfully manage and rapidly recover from energy-related disruptions, while continuing to deliver affordable energy services.…”

Section: Introductionmentioning

confidence: 99%

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Improvingurban Energyresiliencewith an Integrative Framework Based on Machine Learningmethods

Hassan

Megahed²

2022

A&J

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Introduction:Climate change and global warming are among the greatest challenges facing the world today. A newconcept, known as urban resilience, has been developed in response. There are various approaches to urban resilience. Among them, is the urban energy resilience (UER) approach, which poses a considerable challenge. Machine learning (ML), as an application of artificial intelligence (AI), provides powerful and affordable computing resources, large-scale datamining, advanced algorithms, and real-timemonitoring. However, veryfewstudies have investigated howsuch aspectscan be integrated into urban resilience in general, and UER in particular. Purpose of the study: The study develops an integrative framework that can improve UER, based on ML methods. Methodology: We carried out a bibliometric analysisand a systematic review of UER in accordance with AI concepts, models, and applications. Results:The findings of this study were used to create an integrative framework, based on three hierarchical phases, which effectively addressed the main capabilities of UER, identified its priorities, and shed light on how ML can benefit UER as a whole. Novelty:The framework developed in this study also offers insights in integrating ML methods into UER as strategically as possible, especially in the context of climate change and urban energy systems. This framework can serve as reference for specialistsand decision-makers aiming to expand AI and ML applications to optimize UER.

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“…The study found that ML and DL have been successfully applied in assessing the energy efficiency of buildings. (Tien et al, 2022)…”

Section: And DL Methods Of Improving Energy Efficiencymentioning

confidence: 99%