Modelling energy efficiency performance of residential building stocks based on Bayesian statistical inference

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.