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Article

Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models

1
Department of Mechanical Engineering, Heat Engines and Fluids Mechanics, Industrial Engineering School, University of Vigo, Maxwell s/n, 36310 Vigo, Spain
2
Defense University Center, Spanish Naval Academy, Plaza de España, s/n, 36920 Marín, Spain
3
Department of Applied Mathematics I, Telecommunications Engineering School, University of Vigo, 36310 Vigo, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(24), 8968; https://doi.org/10.3390/app10248968
Received: 23 November 2020 / Revised: 11 December 2020 / Accepted: 12 December 2020 / Published: 16 December 2020
The Heat Loss Coefficient (HLC) characterizes the envelope efficiency of a building under in-use conditions, and it represents one of the main causes of the performance gap between the building design and its real operation. Accurate estimations of the HLC contribute to optimizing the energy consumption of a building. In this context, the application of black-box models in building energy analysis has been consolidated in recent years. The aim of this paper is to estimate the HLC of an existing building through the prediction of building thermal demands using a methodology based on Machine Learning (ML) models. Specifically, three different ML methods are applied to a public library in the northwest of Spain and compared; eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR) and Multi-Layer Perceptron (MLP) neural network. Furthermore, the accuracy of the results is measured, on the one hand, using both CV(RMSE) and Normalized Mean Biased Error (NMBE), as advised by AHSRAE, for thermal demand predictions and, on the other, an absolute error for HLC estimations. The main novelty of this paper lies in the estimation of the HLC of a building considering thermal demand predictions reducing the requirement for monitoring. The results show that the most accurate model is capable of estimating the HLC of the building with an absolute error between 4 and 6%. View Full-Text
Keywords: energy efficiency; heat loss coefficient; machine learning; XGBoost; MLP; SVR energy efficiency; heat loss coefficient; machine learning; XGBoost; MLP; SVR
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MDPI and ACS Style

Martínez-Comesaña, M.; Febrero-Garrido, L.; Granada-Álvarez, E.; Martínez-Torres, J.; Martínez-Mariño, S. Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models. Appl. Sci. 2020, 10, 8968. https://doi.org/10.3390/app10248968

AMA Style

Martínez-Comesaña M, Febrero-Garrido L, Granada-Álvarez E, Martínez-Torres J, Martínez-Mariño S. Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models. Applied Sciences. 2020; 10(24):8968. https://doi.org/10.3390/app10248968

Chicago/Turabian Style

Martínez-Comesaña, Miguel, Lara Febrero-Garrido, Enrique Granada-Álvarez, Javier Martínez-Torres, and Sandra Martínez-Mariño. 2020. "Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models" Applied Sciences 10, no. 24: 8968. https://doi.org/10.3390/app10248968

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