Analysis of Heating Expenses in a Large Social Housing Stock Using Artificial Neural Networks

Shaker Zabada and Isam Shahrour
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Shaker Zabada: Economic Department, An-Najah National University, P.O. Box 7, Nablus, Palestine
Isam Shahrour: Laboratoire de Génie Civil et géo-Environnement, Lille University, 59650 Villeneuve d’Ascq, France

Energies, 2017, vol. 10, issue 12, 1-8

Abstract: This paper presents an analysis of heating expenses in a large social housing stock in the North of France. An artificial neural network (ANN) approach is taken for the analysis of heating consumption data collected over four years in 84 social housing residences containing 13,179 dwellings that use collective heating. Analysis provides an understanding of the influence of both physical and socio-economic parameters on heating expenses and proposes a predictive model for these expenses. The model shows that the heating expenses are influenced by both the buildings’ physical parameters and social indicators. Concerning the physical parameters, the most important indicators are the area of the dwellings, followed by the building age and the DPE (energy performance diagnostic). The family size as well as tenant age and income have an important influence on heating expense. The model is then used for establishing a data-based strategy for social housing stock renovation.

Keywords: social housing; heating expenses; artificial neural network; income; energy performance diagnostic; savings; renovation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:10:y:2017:i:12:p:2086-:d:122174

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