Application of a machine learning model in study of energy efficiency in buildings: focus on light construction systems
DOI:
https://doi.org/10.5433/1679-0375.2022v43n1p75Keywords:
Classification and regression tree, Computer simulation, Energy consumptionAbstract
The use of machine learning techniques in thermoenergetic performance studies of buildings emerges as an alternative to conventional methods which analysis require greater data complexity. This research aims to apply a machine learning technique in the study of energy efficiency of a building executed in a light construction system. Thus, an algorithm was implemented referring to an optimized model of classification and regression tree (CART) for application in a data set. This data set includes 2048 parametric simulations of a housing in a light construction system to the climate of the city of São Paulo, whose output indicators are the annual thermal load for heating and the annual thermal load for cooling. From the application of a tree pruning methodology and the use of Grid Search and k-fold Cross Validation techniques, the training and testing of the model was repeated 100 times, thus obtaining average results of 1.11\% of error for heating loads and 1.52\% of error for predicting cooling loads. Subsequently, a sensitivity analysis was performed, revealing the thermal transmittance property of the walls as the parameter with the greatest influence on the prediction of heating load and the condition of contact between the ground and the floor as the parameter with the greatest influence on the prediction of cooling load. Finally, decision trees were generated for visual analysis of strategies that can be adopted to obtain better levels of thermoenergetic performance. Thus, a more simplified diagnosis of energy efficiency was obtained, with low complexity in the interpretation of its results, favoring greater diffusion of the technology in light systems.Metrics
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References
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BRASIL. Ministério do Desenvolvimento, Indústria e Comércio Exterior. Instituto Nacional de Metrologia, Normalização e Qualidade Industrial. Portaria no 42, de 24 de fevereiro de 2021. Instrução Normativa Inmetro para a Classificação de Eficiência Energética de Edificações Comerciais, de Serviços e Públicas (INI-C). Brasília: INMETRO, 2021a. 139 p.
BRASIL. Ministério do Desenvolvimento, Indústria e Comércio Exterior. Instituto Nacional de Metrologia, Normalização e Qualidade Industrial (INMETRO). Consulta pública no18, 12 de julho de 2021. Instrução Normativa Inmetro para a Classificação de Eficiência Energética de Edificações Residenciais (INI-R). Brasília: INMETRO, 2021b. 78 p.
JUSTINO, M. P.; SILVA, F. S.; SILVA RABELO, O. Perspectiva de uso da inteligência artificial (IA) para a eficiência energética em prédios públicos. Cadernos de Prospecção, Salvador: v. 13, n. 3, p. 769, 2020.
MARSLAND, S. Machine learning: an algorithmic perspective. London: Chapman and Hall: CRC, 2011.
MELO, A. P. Desenvolvimento de um método para estimar o consumo de energia de edificações comerciais através da aplicação de redes neurais. 2012. Tese (Doutorado) - Universidade Federal de Santa Catarina, 2012.
NUNES, G. H.; MOURA, J. D. M.; GÜTHS, S.; ATEMA, C.; GIGLIOA, T. Thermo-energetic performance of wooden dwellings: Benefits of cross-laminated timber in Brazilian climates. Journal of Building Engineering, [London], v. 32, p. 101468, 2020. DOI: https://doi.org/10.1016/j.jobe.2020.101468.
OLINGER, M. S. Predição de conforto térmico em escritórios ventilados naturalmente por meio de redes neurais artificiais. 2019. Dissertation (Master's) – University Federal of Santa Catarina, Florianópolis, 2019.
PEDREGOSA, F.; PEDREGOSA, F.; VAROQUAUX, G.; GRAMFORT, A.; MICHEL, V.; THIRION, B.; GRISEL, O.; BLONDEL, M.; PRETTENHOFER, P.; WEISS, R.; DUBOURG, V.; VANDERPLAS, J.; PASSOS, P.; COURNAPEAU, D.; BRUCHER, M.; PERROT, M.; DUCHESNAY, M. Scikit-learn: machine learning in Python. The Journal of machine Learning research, Cambridge, v. 12, p. 2825-2830, 2011.
PHAM, A. D.; NGOC-Tri, N.; HUYNH, N.-T.; TRUONG, T. T. H.; TRUONG, N. C. Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability. Journal of Cleaner Production, Oxford, v. 260, p. 121082, 2020. DOI: https://doi.org/10.1016/j.jclepro.2020.121082.
REFAEILZADEH, P.; TANG, L.; LIU, H. Cross-validation. In: ENCYCLOPEDIA of database systems. New York: Springer-Verlag, Springer, 2009. p. 532–538.
SEYEDZADEH, S.; RAHIMIAN, F. P.; RASTOGIC, P.; GLESKA, I. Tuning machine learning models for prediction of building energy loads. Sustainable Cities and Society, Amsterdam, v. 47, p. 101484, 2019.
SUN, Y.; HAGHIGHAT, F.; FUNG, B. C. A review of the-state-of-the-art in data-driven approaches for building energy prediction. Energy and Buildings, Lausanne, v. 221, p. 110022, 2020.
TSANAS, A.; XIFARA, A. Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy and Buildings, Lausanne, v. 49, p. 560-567, 2012.
WALKER, S.; KHAN, W; KATIC, K. MAASSENA, W.; ZEILER, W. Accuracy of different machine learning algorithms and added-value of predicting aggregated-level energy performance of commercial buildings. Energy and Buildings, Lausanne, v. 209, p. 109705, 2020.
ZARA, R. B. Influência dos parâmetros termofísicos no desempenho térmico de edificações residenciais em sistemas construtivos leves. 2019. Master’s (Dissertation in Civil Engineering - Londrina State University, Londrina, 2019.
ZEKIC-SUSAC, M.; MITROVIC, S.; HAS, A. Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities. International Journal of Information Management, Amsterdam, v. 58, p. 102074, 2021.
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Published
2022-06-06
How to Cite
Moro, G. N., Martins, R. dos S. V., & Giglio, T. G. F. (2022). Application of a machine learning model in study of energy efficiency in buildings: focus on light construction systems. Semina: Ciências Exatas E Tecnológicas, 43(1), 75–84. https://doi.org/10.5433/1679-0375.2022v43n1p75
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