Modelling energy efficiency performance of residential building stocks based on Bayesian statistical inference. (September 2016)

• Record Type: Journal Article
• Authors: Braulio-Gonzalo, Marta; Juan, Pablo; Bovea, María D.; Ruá, María José
• Is Part Of: Environmental modelling & software. Volume 83(2016:Sep.)
• Page Start: 198
• Page End: 211
• Publication Date: 2016-09
• Subjects: Energy efficiency -- Residential building stock -- Bayesian inference -- INLA; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.05.018
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This paper provides a model based on Integrated Nested Laplace Approximation to predict the energy performance of existing residential building stocks. The energy demand and the discomfort hours for heating and cooling were taken as response variables and five parameters were considered as potentially significant to assess the building energy performance: urban block pattern, street height-width ratio, building class through the building shape factor, year of construction and solar orientation of the main façade. A total of 240 dynamic energy simulations were run varying these parameters, by using the EnergyPlus software with the Design Builder interface, which allowed the response variables to be determined for a set of sample buildings. Simulation results revealed the most and least significant parameters in the energy performance of the buildings. The model developed is a useful decision-making tool in assisting local authorities during energy refurbishment interventions at the urban scale. Highlights: A model to predict the energy performance of residential building stocks is developed. The energy demand and the discomfort hours were considered as output of the model. Bayesian inference based on INLA was the statistical framework of the model. The model is useful to identify urban areas that require urgent energy refurbishment.