Modelling energy efficiency performance of residential building stocks based on Bayesian statistical inference
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Otros documentos de la autoría: Braulio-Gonzalo, Marta; Juan, Pablo; Bovea, María D; Ruá, María José
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7035
comunitat-uji-handle3:10234/8617
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Título
Modelling energy efficiency performance of residential building stocks based on Bayesian statistical inferenceFecha de publicación
2016Editor
ElsevierISSN
1364-8152; 1873-6726Cita bibliográfica
BRAULIO-GONZALO, Marta, et al. Modelling energy efficiency performance of residential building stocks based on Bayesian statistical inference. Environmental Modelling & Software, 2016, vol. 83, p. 198-211.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.sciencedirect.com/science/article/pii/S1364815216301542Palabras clave / Materias
Resumen
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 ... [+]
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. [-]
Publicado en
Environmental Modelling & Software, 2016, vol. 83Derechos de acceso
Copyright © Elsevier B.V.
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