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,Alexis Pérez-Fargallo

doi:10.3390/su13052426

David Bienvenido-Huertas, Jesús A Pulido-Arcas + Show 2 more

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https://doi.org/10.3390/su13052426

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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.

Highlights

The specific objective of this study is to test the accuracy of six regression algorithms to predict the risk of fuel poverty, expressed by the fuel poverty potential risk index (FPPRI) (i) multilayer perceptron (MLP); (ii) K-nearest neighbors (K-NN); (iii) classification and regression tree (CART); (iv) random forest (RF); (v) M5P; and (vi) support vector regression (SVR)
The results revealed that both the CART and the MLP model obtained better performance than the linear regression; (ii) Mousa et al [72] analyzed the use of a CART model to estimate the air change rate in buildings; and (iii) in another study [73], a CART model was developed to predict the monthly energy consumption in residential buildings
The present exploratory study set out with the aim of clarifying the best algorithm to predict FPPRI of financially deprived households in Chile. Considering their performance, it can be concluded that multilayer perceptron, support vector regression and M5P are, in this order, the best option

Summary

Introduction

Called fuel poverty or energy vulnerability, takes place when households cannot keep comfortable temperatures inside their homes or cannot access energy services at a reasonable cost [1,2]. This phenomenon has received the attention of the scientific community and society in recent years due to its political and social implications [3,4]. Previous research has shown that energy poverty is a driver for the physical deterioration of residents [5,6] and is even responsible for a higher death rate in winter due to the poor thermal conditions inside buildings [7,8]. There is a wide consensus about the fact that energy poverty stems from a combination of high-energy prices, low family income, inefficient buildings, and outdated electrical household appliances [9,10].

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