LAUSR

Abstract Many studies are focused on the diagnosis of fuel poverty. However, its prediction before occupying households is a developing research area. This research studies the feasibility of implementing the Fuel Poverty Potential Risk Index (FPPRI) in different climate zones of Chile by means of regression models based on artificial neural networks (ANNs). A total of 116,640 representative case studies were carried out in the three cities with the largest population in Chile: Santiago, Concepcion, and Valparaiso. Apart from energy price (EP) and income (IN), 9 variables related to the morphology of the building were considered in approach 1. Furthermore, approach 2 was developed by including comfort hours (NCH). A total of 84 datasets were combined considering both approaches and the 5 most unfavourable deciles according to the income level of Chilean families. The results of both approaches showed a better performance in the use of individual models for each climate (MLPC, MLPS, and MLPV), and the dataset with all deciles (Full) could be used. Regarding the influence of the input variables on the models, IN was the most determinant, and NCH becomes important in approach 2. The potential of using this methodology to allocate social housing would guarantee the main objective of the country: the reduction of fuel poverty in the roadmap for 2050.