@article{35303,
  keywords = {India, Machine learning, COVID-19, Work-from-home, NILM, Mixture models},
  author = {Ramit Debnath and Ronita Bardhan and Ashwin Misra and Tianzhen Hong and Vida Rozite and Michael H Ramage},
  title = {Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models},
  abstract = {<p>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.</p>},
  year = {2022},
  journal = {Energy Policy},
  volume = {164},
  pages = {112886},
  month = {05/2022},
  issn = {03014215},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S0301421522001112},
  doi = {10.1016/j.enpol.2022.112886},
  language = {eng},
}