Applied Machine Learning Algorithms for Courtyards Thermal Patterns Accurate Prediction

Eduardo Diz-Mellado, Samuele Rubino, Soledad Fernández-García, Macarena Gómez-Mármol, Carlos Rivera-Gómez and Carmen Galán-Marín
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Eduardo Diz-Mellado: Departamento de Construcciones Arquitectónicas 1, Escuela Técnica Superior de Arquitectura, Universidad de Sevilla, Avda. Reina Mercedes, 2, 41012 Seville, Spain
Samuele Rubino: Departamento de Ecuaciones Diferenciales y Análisis Numérico, Facultad de Matemáticas and Instituto de Matemáticas (IMUS), Universidad de Sevilla, C/Tarfia, s/n, 41012 Seville, Spain
Soledad Fernández-García: Departamento de Ecuaciones Diferenciales y Análisis Numérico, Facultad de Matemáticas and Instituto de Matemáticas (IMUS), Universidad de Sevilla, C/Tarfia, s/n, 41012 Seville, Spain
Macarena Gómez-Mármol: Departamento de Ecuaciones Diferenciales y Análisis Numérico, Facultad de Matemáticas and Instituto de Matemáticas (IMUS), Universidad de Sevilla, C/Tarfia, s/n, 41012 Seville, Spain
Carlos Rivera-Gómez: Departamento de Construcciones Arquitectónicas 1, Escuela Técnica Superior de Arquitectura, Universidad de Sevilla, Avda. Reina Mercedes, 2, 41012 Seville, Spain
Carmen Galán-Marín: Departamento de Construcciones Arquitectónicas 1, Escuela Técnica Superior de Arquitectura, Universidad de Sevilla, Avda. Reina Mercedes, 2, 41012 Seville, Spain

Mathematics, 2021, vol. 9, issue 10, 1-19

Abstract: Currently, there is a lack of accurate simulation tools for the thermal performance modeling of courtyards due to their intricate thermodynamics. Machine Learning (ML) models have previously been used to predict and evaluate the structural performance of buildings as a means of solving complex mathematical problems. Nevertheless, the microclimatic conditions of the building surroundings have not been as thoroughly addressed by these methodologies. To this end, in this paper, the adaptation of ML techniques as a more comprehensive methodology to fill this research gap, covering not only the prediction of the courtyard microclimate but also the interpretation of experimental data and pattern recognition, is proposed. Accordingly, based on the climate zoning and aspect ratios of 32 monitored case studies located in the South of Spain, the Support Vector Regression (SVR) method was applied to predict the measured temperature inside the courtyard. The results provided by this strategy showed good accuracy when compared to monitored data. In particular, for two representative case studies, if the daytime slot with the highest urban overheating is considered, the relative error is almost below 0.05%. Additionally, values for statistical parameters are in good agreement with other studies in the literature, which use more computationally expensive CFD models and show more accuracy than existing commercial tools.

Keywords: courtyard; climate change; microclimate; Support Vector Regression (SVR); machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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