Machine Learning-based Approach for Predicting Energy Poverty in the United Kingdom Incorporating Remote-Sensing and Socioeconomic Data

Energy poverty affects billions worldwide, including people in developed and developing countries. Identifying those living in energy poverty and implementing successful solutions require timely and detailed survey data, which can be costly, time-consuming, and difficult to obtain, particularly in rural areas. Through machine learning, this study investigates the possibility of identifying vulnerable households by combining satellite remote sensing with socioeconomic survey data in the UK. In doing so, this research develops a machine learning-based approach to predicting energy poverty in the UK using the low Income low energy efficiency (LILEE) indicator derived from a combination of remote sensing and socioeconomic data. Data on energy consumption, building characteristics, household income, and other relevant variables at the local authority level are fused with geospatial satellite imagery. The findings indicate that a machine learning algorithm incorporating geographical and environmental information can predict approximately 83% of districts with significant energy poverty. This study contributes to the expanding body of research on energy poverty prediction and can help shape policy and decision-making for energy efficiency and social fairness in the UK and worldwide