Scrutiny of Electricity Billing and Supply Data as a Probable Proxy for Economic Activities: A Comprehensive Analysis of Power Consumption of Dhaka, Bangladesh

Abstract

This is a case study of electricity consumption pattern of dwellers of Dhaka, the largest city in Bangladesh and one of the most densely populated and fastest growing cities of the world. Bangladesh lacks data driven research that sheds light on various aspects of electricity ecosystem. The gap between the need for such research and energy policy initiatives is ever increasing. This paper aims to reduce this gap. This paper is also seminal in the context of Bangladesh energy ecosystem because of the use of a large dataset to find the policy relevant outcomes. Additionally, by analyzing the consumption pattern, this paper indirectly attempts to understand the economic health of the households. The work shows, it is possible to use this consumption data for better understanding of country’s economy. The approach can be an effective measure of household level economic condition, especially because various other direct methods such as survey may be expensive and time consuming. This work harnesses a uniquely built dataset based on monthly billing data (from 2005-2017) at the household level and hourly supply data. The underlying assumption of the paper regarding better energy policy for managing public utilities, such as electricity, requires fulfillment of a couple of prerequisites. Understanding the electricity demand at a micro level is one such prerequisite. To do so one needs to, firstly, understand the consumer behavior and how the factors, e.g., their economic health, household sizes, level of education, weather etc., influence the household electricity demand. Secondly, one needs to understand how regulatory decisions impact consumption behavior. And final prerequisite is to develop reliable short, mid, and long-term demand forecasting models. A heterogeneous dataset that includes the uniquely built consumption and supply dataset, Household Income and Expenditure Survey data, population census data, and household appliance usage survey data is examined using both traditional statistical and machine learning methods to help policy makers gain more insight into these prerequisites. The research finds that the number of consumers is rapidly increasing over the years. However, most of them belong to specific groups (in term of consumption level and dwelling location) that have predictable patterns. High consumption users belong to mainly in two specific zones where total numbers of users are very low with respect to other zones. It is found that lower level household consumption pattern is impacted by weather fluctuations. The impact of regulatory intervention is measured by scrutinizing impact of price hike at the household level. The research indicates that short term impact of price hike on electricity consumption is very small (inelastic). However, impact varies among tariff groups. The research also proposes a computational approach to forecast short term electricity demand at household level.