LAUSR

Abstract Demand-side management (DSM) has significant potential to reduce peak electricity demand to achieve cost-efficient and less-emissions-intensive electricity generation, but adoption is not yet widespread, especially in developing countries. This is due in part to a lack of detailed knowledge of the household factors that shape demand patterns, which are not necessarily the same as those in developed nations. This paper reports on the compilation and analysis of residential electricity consumption data to identify the factors that are most responsible for driving daily demand patterns in Bangladeshi households. In the absence of smart meters, structured interviews with householders were used to estimate daily demand patterns. Time-segmented regression analysis then identified the dominant factors that influence demand at different timeslots across the day. The dominant factors were found to be the number of major electrical appliances and the number of occupants in residences. We found that daily average electricity consumption is mostly influenced by the number of major electrical appliances at houses, while residential demand during system peaks is dominated by the number of occupants. The results are not nationally applicable due to the small and non-representative data set but indicate how this approach could be used to design targeted DSM strategies.