Machine Learning-Enhanced Measuring of Multidimensional Energy Poverty: Insights from a Pilot Survey in Portugal and Denmark

Rahil Dejkam and Reinhard Madlener ()
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Rahil Dejkam: RWTH Aachen University
Reinhard Madlener: RWTH Aachen University

A chapter in Operations Research Proceedings 2024, 2025, pp 242-247 from Springer

Abstract: Abstract Energy poverty, a multidimensional socio-economic challenge, significantly affects the welfare of many people across Europe. This paper aims to alleviate energy poverty by exploring sustainable energy practices and policy interventions, using pilot household survey data from Portugal and Denmark. A Novel Multidimensional energy poverty index (MEPI) is developed to assess energy poverty through different dimensions such as heating and cooling comfort, financial strain, access to energy-efficient appliances, and overall health and well-being. Using machine learning techniques, key features are selected. Subsequently, a logistic regression model is used to predict energy-poor households based on selected socio-economic, and policy-related factors. Results indicate that sustainable energy-saving behaviors and supportive government policies can mitigate energy poverty. Furthermore, for analyzing the impact of determined features the Shapley additive explanations (SHAP) method is being utilized. Finally, the main findings are evaluated further via scenario simulation analysis.

Keywords: Multidimensional Energy Poverty Index; Thermal Comfort; Sustainable Energy-saving Practices; Machine Learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-92575-7_34

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DOI: 10.1007/978-3-031-92575-7_34

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