Variations in Residential Electricity Demand Across Income Categories in Urban Bangalore: Results from Primary Survey

Peer Reviewed
1 December 2019

Sashi Kiran Challa, Shoibal Chakravarty, Kshitija Joshi

Residential electricity demand arises from the need for households to meet various end uses. This demand from residences has seen consistent growth over the last decade. In developing nations like India, this demand is also a significant contributor to greenhouse emissions, given that we are starting from a lower base compared to other countries. Our understanding in this space though is still limited. One of the key reasons for this is lack of availability of open data sources on household's ownership, usage patterns of appliances and key socio-economic indicators which are critical in gaining insights into demand patterns of households.

We outline the methodology for designing and executing a statistically representative survey to collect data to understand and analyze residential electricity demand, presenting the case study of a primary representative survey we conducted in urban Bangalore covering 403 households. We also present the methodology for development of a model to build residential load curves at hourly resolution. We analyze three primary questions; how do households in different income-representative brackets consume electricity, what is the variation in consumption between these brackets and what are key appliance groups that contribute to this variance. We also look at generation profiles from solar PV installations to see what times of the day demand can be met from these renewable sources. In the process, we also examine the rooftop policy in Karnataka and suggest any amendments that can be made to increase adoption of solar rooftop to meet some of this demand locally, based on the analysis of load curves.


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Publication reference
Challa, S. K., Chakravarty, S., & Joshi, K. (2019). Variations in Residential Electricity Demand Across Income Categories in Urban Bangalore: Results from Primary Survey. 2019 26th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW). doi:10.1109/hipcw.2019.00009

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Publication | 1 May 2020