The article “Towards an airtightness compliance tool based on machine learning models for naturally ventilated dwellings”, co-authored by Vítor Cardoso of BUILT CoLAB (article resulting from his work as part of the PhD he completed in 2022 at FEUP – Faculty of Engineering of the University of Porto), is available online and free to access, together with Lurdes Simões (Construct, FEUP), Nuno Ramos (Construct, FEUP), Ricardo Almeida (Construct, IPViseu), Manuela Almeida (ISISE, UMINHO), Luís sanhudo (BUILT CoLAB) and João Fernandes (FEUP).
This article will be published in Volume 285 of Elsevier’s “Energy and Buildings” magazine in April 2023.
Article abstract:
Physical models and probabilistic applications often guide the study and characterization of natural phenomena in engineering. Such is the case of the study of air change rates (ACHs) in buildings for their complex mechanisms and high variability. It is not uncommon for the referred applications to be costly and impractical in both time and computation, resulting in the use of simplified methodologies and setups. The incorporation of airtightness limits to quantify adequate ACHs in national transpositions of the Energy Performance Building Directive (EPBD) exemplifies the issue. This research presents a roadmap for developing an alternative instrument, a compliance tool built with a Machine Learning (ML) framework, that overcomes some simplification issues regarding policy implementation while fulfilling practitioners’ needs and general societal use. It relies on dwellings’ terrain, geometric and airtightness characteristics, and meteorological data. Results from previous work on a region with a mild heating season in southern Europe apply in training and testing the proposed tool. The tool outputs numerical information on the air change rates performance of the building envelope, and a label, accordingly. On the test set, the best regressor showed mean absolute errors (MAE) below 1.02% for all the response variables, while the best classifier presented an average accuracy of 97.32%. These results are promising for the generalization of this methodology, with potential for application at regional, national, and European Union levels. The developed tool could be a complementary asset to energy certification programmes of either public or private initiatives.
The full article is available here.
