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Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models

Debnath, Ramit and Bardhan, Ronita and Misra, Ashwin and Hong, Tianzhen and Rozite, Vida and Ramage, Michael 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 . Art. No. 112886. ISSN 0301-4215. PMCID PMC9022708. doi:10.1016/j.enpol.2022.112886.

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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.

Item Type:Article
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URLURL TypeDescription CentralArticle
Debnath, Ramit0000-0003-0727-5683
Bardhan, Ronita0000-0001-5336-4084
Misra, Ashwin0000-0003-0554-9409
Hong, Tianzhen0000-0003-1886-9137
Additional Information:© 2022 Published by Elsevier. Received 15 November 2021, Revised 23 February 2022, Accepted 25 February 2022, Available online 17 March 2022, Version of Record 17 March 2022. This study is supported by Bill and Melinda Gates Foundation through the Gates Cambridge Scholarship awarded to RD under the grant number OPP1144 and Churchill College Sustainability Fellowship. RD would like to thank Caltech for hosting him as a visiting faculty associate in Computational Social Science for 2021–22. In addition, RB and RD received funding from the Isaac Newton Trust through the Energy Transition Small Grant – 2020. RD and MH would also like to thank the Laudes Foundation for supporting this study through the ‘Growing the Future’ project at the University of Cambridge. RB would further like to thank the UK Space Agency for funding her work. All opinions, findings and conclusions are that of the authors and do not necessarily reflect the views of the funding organisation and authors' affiliated institutions. CRediT authorship contribution statement. Ramit Debnath: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Funding acquisition. Ronita Bardhan: Conceptualization, Methodology, Validation, Formal analysis, Writing – review & editing, Visualization, Supervision, Funding acquisition. Ashwin Misra: Data curation. Tianzhen Hong: Writing – review & editing. Vida Rozite: Writing – review & editing. Michael H. Ramage: Writing – review & editing, Funding acquisition. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Funding AgencyGrant Number
Bill and Melinda Gates FoundationOPP1144
Churchill CollegeUNSPECIFIED
Isaac Newton TrustUNSPECIFIED
Laudes FoundationUNSPECIFIED
United Kingdom Space Agency (UKSA)UNSPECIFIED
Subject Keywords:COVID-19; Work-from-home; NILM; Machine learning; Mixture models; India
PubMed Central ID:PMC9022708
Record Number:CaltechAUTHORS:20220317-376372000
Persistent URL:
Official Citation:Ramit Debnath, Ronita Bardhan, Ashwin Misra, Tianzhen Hong, Vida Rozite, Michael H. Ramage, Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models, Energy Policy, Volume 164, 2022, 112886, ISSN 0301-4215,
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113946
Deposited By: George Porter
Deposited On:18 Mar 2022 17:56
Last Modified:31 May 2022 18:20

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