Show simple item record

dc.contributor.authorDebnath, Ramit
dc.contributor.authorBardhan, Ronita
dc.contributor.authorMisra, Ashwin
dc.contributor.authorHong, Tianzhen
dc.contributor.authorRozite, Vida
dc.contributor.authorRamage, Michael H
dc.date.accessioned2022-03-18T00:30:29Z
dc.date.available2022-03-18T00:30:29Z
dc.date.issued2022-05
dc.identifier.issn0301-4215
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/335143
dc.description.abstractThis 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.
dc.publisherElsevier BV
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleLockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models.
dc.typeArticle
dc.publisher.departmentDepartment of Architecture
dc.date.updated2022-03-17T07:58:41Z
prism.endingPage112886
prism.number112886
prism.publicationDate2022
prism.publicationNameEnergy Policy
prism.startingPage112886
prism.volume164
dc.identifier.doi10.17863/CAM.82575
dcterms.dateAccepted2022-02-25
rioxxterms.versionofrecord10.1016/j.enpol.2022.112886
rioxxterms.versionVoR
dc.contributor.orcidDebnath, Ramit [0000-0003-0727-5683]
dc.contributor.orcidBardhan, Ronita [0000-0001-5336-4084]
dc.identifier.eissn1873-6777
rioxxterms.typeJournal Article/Review
cam.issuedOnline2022-03-17
cam.orpheus.success2022-03-17 - Embargo set during processing via Fast-track
cam.depositDate2022-03-17
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International