气候变化领域集成服务门户(CCPortal): Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models

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DOI	10.1016/j.enpol.2022.112886
	Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models
	Debnath R.; Bardhan R.; Misra A.; Hong T.; Rozite V.; Ramage M.H.
发表日期	2022
ISSN	0301-4215
卷号	164
英文摘要	This study evaluates the effect of complete nationwide lockdown in 2020 on residential electricity demand across 13 Indian cities and the role of digitalisation using a public smart meter dataset. We undertake a data-driven approach to explore the energy impacts of work-from-home norms across five dwelling typologies. Our methodology includes climate correction, dimensionality reduction and machine learning-based clustering using Gaussian Mixture Models of daily load curves. Results show that during the lockdown, maximum daily peak demand increased by 150–200% as compared to 2018 and 2019 levels for one room-units (RM1), one bedroom-units (BR1) and two bedroom-units (BR2) which are typical for low- and middle-income families. While the upper-middle- and higher-income dwelling units (i.e., three (3BR) and more-than-three bedroom-units (M3BR)) saw night-time demand rise by almost 44% in 2020, as compared to 2018 and 2019 levels. Our results also showed that new peak demand emerged for the lockdown period for RM1, BR1 and BR2 dwelling typologies. We found that the lack of supporting socioeconomic and climatic data can restrict a comprehensive analysis of demand shocks using similar public datasets, which informed policy implications for India's digitalisation. We further emphasised improving the data quality and reliability for effective data-centric policymaking. © 2022
英文关键词	COVID-19; India; Machine learning; Mixture models; NILM; Work-from-home
语种	英语
scopus关键词	Climate models; Electric load management; Electric power measurement; Electric power utilization; Locks (fasteners); Machine learning; Public policy; Smart meters; COVID-19; Data driven; Gaussian Mixture Model; India; Mixture modeling; NILM; Nonintrusive load monitoring; Peak demand; Residential Electricity demands; Work-from-home; Housing
来源期刊	Energy Policy
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/256270
作者单位	Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge, UK, Cambridge, CB2 1AG, United Kingdom; Centre for Natural Material Innovation, Department of Architecture, University of Cambridge, UK, Cambridge, CB2 1PX, United Kingdom; Division of Humanities and Social Science, California Institute of Technology, Pasadena, CA 91125, United States; Sustainable Design Group, Department of Architecture, University of Cambridge, UK, Cambridge, CB2 1PX, United Kingdom; The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213-3890, United States; Building Technology and Urban Systems Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States; Energy Efficiency Division, International Energy Agency, Paris, 75015, France
推荐引用方式 GB/T 7714	Debnath R.,Bardhan R.,Misra A.,et al. Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models[J],2022,164.
APA	Debnath R.,Bardhan R.,Misra A.,Hong T.,Rozite V.,&Ramage M.H..(2022).Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models.Energy Policy,164.
MLA	Debnath R.,et al."Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models".Energy Policy 164(2022).