2024-03-29T10:37:38Zhttps://repositori.uji.es/oai/requestoai:repositori.uji.es:10234/1608922023-10-24T12:09:34Zcom_10234_7035com_10234_9col_10234_8617
00925njm 22002777a 4500
dc
Braulio-Gonzalo, Marta
author
Juan, Pablo
author
Bovea, María D
author
Ruá, María José
author
2016
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.
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.
1364-8152
1873-6726
http://hdl.handle.net/10234/160892
http://dx.doi.org/10.1016/j.envsoft.2016.05.018
energy efficiency
residential building stock
Bayesian inference
INLA
Modelling energy efficiency performance of residential building stocks based on Bayesian statistical inference