Thermal comfort prediction in a building category: Artificial neural network generation from calibrated models for a social housing stock in southern Europe

A significant part of the housing stock in southern Europe is obsolete and in need of extensive retrofitting to improve its energy performance and thermal comfort. However, before adequate retrofit measures can be proposed for this housing stock, the characterization of current building performance is fundamental. Although the simulation tools frequently used and widely accepted by the scientific community ensure accurate results, these require high computational times. The main aim of this paper is the development of a surrogate model to speed up the thermal comfort prediction for any member of a building category, ensuring high reliability by testing the entire simulation process with real data measured in-situ. To this end, an artificial neural network (ANN) is generated under MATLAB (R) environment using the data obtained from EnergyPlus simulations for linear-type social housing multi-family buildings in southern Spain, which were constructed in the post-war period. The developed ANN provides a regression coefficient between simulation targets and ANN outputs of 0.96, with a relative error between monitored and simulated data below 9%. A further result is that the building category characterization shows a general lack of suitable indoor thermal comfort conditions, thereby showing the great need for effective retrofit strategies.