Towards an Approach for Fuel Poverty Detection from Gas Smart Meter Data using Decision Tree Learning

Abstract

Energy poverty has a negative impact on the health and well-being of individuals within a household; affecting not only comfort levels but also results in increased levels of seasonal mortality. Energy poverty issues are receiving increasing attention and particular interest has focused on identifying how the needs of people in vulnerable situations can be improved by suppliers and public institutions. As such, in this paper, the focus of the research is towards a prediction of whether an individual household is in a poverty situation through the analysis of their gas smart meter data. To achieve this prediction, decision trees and cloud analytics are employed to detect and individual's socio-economic standing and whether they receive government assistance in paying for their bills. The results demonstrated a 74.2% AUC classification using a Two-Class Decision Forest to detect social class and an 88.1% AUC classification using a two-class decision forest to detect whether the household is in receipt of government funding.