Influence of Geodemographic Factors on Electricity Consumption and Forecasting Models

Abstract

The residential sector is a major consumer of electricity, and its demand will rise by 65 percent by the end of 2050. The electricity consumption of a household is determined by various factors, e.g. house size, socio-economic status of the family, size of the family, etc. These factors play a critical role in analyzing the energy consumption causalities in the residential sector for better energy prediction models, effective price policy implementations, and improved customer engagement in energy efficiency programs. However, determining the effect of demographic factors on energy consumption is a challenging prospect. First, it is not trivial to study the causes of energy consumption variation, even for similar size residential houses, without analyzing the impact of interdependencies between demographic factors on energy consumption behavior. Second, to achieve higher accuracy of energy prediction models, it is necessary to identify key geodemographic factors that influence these models. Previous studies have only identified a limited number of socio-economic and dwelling factors. In this paper, we study the significance of 826 <i>geodemographic</i> factors on electricity consumption for 4917 homes in the City of London. <i>Geodemographic factors</i> cover a wide array of categories e.g. social, economic, dwelling, family structure, health, education, finance, occupation, and transport. Using Spearman correlation, we have identified 354 factors that are strongly correlated with electricity consumption. We also examine the impact of using <i>geodemographic</i> factors in designing forecasting models. In particular, we develop an encoder-decoder LSTM model which shows improved accuracy with geodemographic factors. We believe that our study will help energy companies design better energy management strategies.