Thermal comfort prediction of the existing housing stock in southern Spain through calibrated and validated parameterized simulation models

• Published in: Energy and buildings 2022-01, Vol.254, p.111562, Article 111562
• Main Authors: Calama-González, Carmen María, Suárez, Rafael, León-Rodríguez, Ángel Luis
• Format: Article
• Language: English
• Subjects: Bayesian calibration; Bottom-up; Building stock modelling; Computer applications; Data collection; Discomfort; Energy; Housing; Hypercubes; Infiltration rate; International standards; Mediterranean climate; Parameter identification; Parameterization; Public housing; Retrofitting; Samples; Simulation; Social housing stock; Statistical analysis; Statistical methods; Supercooling; Thermal comfort; Typology
• ISSN: 0378-7788; 1872-6178
• DOI: 10.1016/j.enbuild.2021.111562

•Sensitivity analysis and Bayesian approaches are used for model calibration.•The building stock performance is predicted through a hybrid bottom-up method.•Predicted average annual discomfort hours for H-typology in southern Spain is 68%.•The most influential modelling parameters on thermal comfort are obtained.•10 Latin Hypercube Samples per parameterized variable report solid results. Due to 2030 and 2050 targets of the latest international standards, energetically retrofitting the existing building sector requires special attention. Prior to the proposal of retrofit strategies, it is necessary to analyse the current energy performance of the stock. Although simulation tools provide accurate results of energy performance at the building level, individual assessments of the large-scale stock lead to extensive data collection and huge computational resources. This paper assesses the current performance of one of the most representative building typologies in social housing stock in southern Spain, the H-typology, predicting results on indoor thermal comfort at the stock level. The physical, constructive and geometrical building characterisation and the selection of a calibrated and validated case study through monitoring are used to generate parameterized energy simulation models, providing statistically representative samples of the stock. Open-access energy simulation tools have been combined with statistical software. Conclusions reported show average annual discomfort hours of around 68%, with higher percentage of annual undercooling discomfort hours, and identify the most influential parameters on indoor thermal comfort as infiltration rate, people density and night-time natural ventilation rate. Moreover, 10 Latin Hypercube Samples per parameterized variable derived in highly representative results for thermally analysing the stock.