Energy poverty prediction in the United Kingdom: A machine learning approach

Dlzar Al Kez, Aoife Foley, Zrar Khald Abdul and Dylan Furszyfer Del Rio

Energy Policy, 2024, vol. 184, issue C

Abstract: 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.

Keywords: Energy poverty; machine learning; predictive model; random forest; socioeconomic data; remote sensing (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:enepol:v:184:y:2024:i:c:s0301421523004949

DOI: 10.1016/j.enpol.2023.113909

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