EURO 2024 Copenhagen
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3584. Integrating machine learning in measuring multidimensional energy poverty: new insights from a survey analysis in Europe

Invited abstract in session WC-6: Advancements of OR-analytics in statistics, machine learning and data science 18, stream Advancements of OR-analytics in statistics, machine learning and data science.

Wednesday, 12:30-14:00
Room: 1013 (building: 202)

Authors (first author is the speaker)

1. Rahil dejkam
School of Business and Economics / E.ON Energy Research Center, RWTH Aachen University
2. Reinhard Madlener
School of Business and Economics / E.ON Energy Research Center, RWTH Aachen University

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 household survey data from Portugal and Denmark. A 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. In a next step, for selecting features, machine learning techniques are employed including recursive feature elimination and random forest analysis. These methods help to reduce the number of irrelevant and mutually correlated predictors. Subsequently, a logistic regression model is used to predict energy-poor households based on selected socio-economic, and policy-related factors. The logistic regression 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. The result shows that fully adopting waste-compositing and energy-efficient microwave ovens can decrease the proportion of energy-poor households by 93% and 79%, respectively.

Keywords

Status: accepted

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