Deriving electricity consumption patterns using a decomposition approach

Abstract

Studying the effectiveness of certain policies on load management requires detailed knowledge about the consumption pattern of end-use sectors. The consumption pattern includes both short-time scale variations and long-term trends in electricity consumption. This paper proposes a decomposition method to extract the seasonal (spring, summer cooling, autumn, and winter heating), day-of-week (working day, weekend), and time-of-day load curves of end-use sectors from aggregate load data of the electricity distribution network. The obtained data is used to study variations in the consumption patterns of end-use sectors such as households. The estimated load profiles represent the behavior of a large group of consumers for several years. The model is used to disaggregate the load data of Tehran province in Iran from 2006 to 2018. Resultsshow that during years, industrial power consumption has decreased during peak hours at night due to the shrinkage of two-shift industries. By contrast, the households’ load profiles in the autumn and spring load have shifted upward. This is the net effect of reduced lighting electricity consumption and increased access to electricity-consuming devices. Regression analysis on load decomposition results shows a mild effect of energy subsidy reform on lighting and cooling demand.