Prediction of Fuel Poverty Potential Risk Index Using Six Regression Algorithms: A Case-Study of Chilean Social Dwellings

David Bienvenido-Huertas, Jesús A. Pulido-Arcas, Carlos Rubio-Bellido and Alexis Pérez-Fargallo
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David Bienvenido-Huertas: Department of Building Construction II, University of Seville, 41012 Seville, Spain
Jesús A. Pulido-Arcas: Graduate School of Arts and Sciences, University of Tokyo, Tokyo 153-8902, Japan
Carlos Rubio-Bellido: Department of Building Construction II, University of Seville, 41012 Seville, Spain
Alexis Pérez-Fargallo: Department of Building Science, University of Bío-Bío, Concepción 410300, Chile

Sustainability, 2021, vol. 13, issue 5, 1-30

Abstract: In recent times, studies about the accuracy of algorithms to predict different aspects of energy use in the building sector have flourished, being energy poverty one of the issues that has received considerable critical attention. Previous studies in this field have characterized it using different indicators, but they have failed to develop instruments to predict the risk of low-income households falling into energy poverty. This research explores the way in which six regression algorithms can accurately forecast the risk of energy poverty by means of the fuel poverty potential risk index. Using data from the national survey of socioeconomic conditions of Chilean households and generating data for different typologies of social dwellings (e.g., form ratio or roof surface area), this study simulated 38,880 cases and compared the accuracy of six algorithms. Multilayer perceptron, M5P and support vector regression delivered the best accuracy, with correlation coefficients over 99.5%. In terms of computing time, M5P outperforms the rest. Although these results suggest that energy poverty can be accurately predicted using simulated data, it remains necessary to test the algorithms against real data. These results can be useful in devising policies to tackle energy poverty in advance.

Keywords: fuel poverty potential risk index; multilayer perceptron; K -nearest neighbors; tree models; support vector regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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