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

Bienvenido-Huertas, David; Pulido Arcas, Jesús Alberto; Rubio-Bellido, Carlos; Perez-Fargallo, Alexis

Keywords: k-nearest neighbors, multilayer perceptron, support vector regression, fuel poverty potential risk index, tree models

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.

Más información

Título de la Revista: SUSTAINABILITY
Volumen: 13
Número: 5
Editorial: MDPI
Fecha de publicación: 2021
Idioma: Inglés
Financiamiento/Sponsor: Conicyt Fondecyt Regular 1200551
DOI:

10.3390/su13052426

Notas: WOS