Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models
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 consump...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/24/8968 |
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| author | Miguel Martínez-Comesaña Lara Febrero-Garrido Enrique Granada-Álvarez Javier Martínez-Torres Sandra Martínez-Mariño |
| author_facet | Miguel Martínez-Comesaña Lara Febrero-Garrido Enrique Granada-Álvarez Javier Martínez-Torres Sandra Martínez-Mariño |
| author_sort | Miguel Martínez-Comesaña |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-10T14:01:40Z |
| format | Article |
| id | doaj.art-0c115c1d6107494980c21a6e15e31dc3 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T14:01:40Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0c115c1d6107494980c21a6e15e31dc32023-11-21T00:59:46ZengMDPI AGApplied Sciences2076-34172020-12-011024896810.3390/app10248968Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning ModelsMiguel Martínez-Comesaña0Lara Febrero-Garrido1Enrique Granada-Álvarez2Javier Martínez-Torres3Sandra Martínez-Mariño4Department of Mechanical Engineering, Heat Engines and Fluids Mechanics, Industrial Engineering School, University of Vigo, Maxwell s/n, 36310 Vigo, SpainDefense University Center, Spanish Naval Academy, Plaza de España, s/n, 36920 Marín, SpainDepartment of Mechanical Engineering, Heat Engines and Fluids Mechanics, Industrial Engineering School, University of Vigo, Maxwell s/n, 36310 Vigo, SpainDepartment of Applied Mathematics I, Telecommunications Engineering School, University of Vigo, 36310 Vigo, SpainDepartment of Mechanical Engineering, Heat Engines and Fluids Mechanics, Industrial Engineering School, University of Vigo, Maxwell s/n, 36310 Vigo, SpainThe 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%.https://www.mdpi.com/2076-3417/10/24/8968energy efficiencyheat loss coefficientmachine learningXGBoostMLPSVR |
| spellingShingle | Miguel Martínez-Comesaña Lara Febrero-Garrido Enrique Granada-Álvarez Javier Martínez-Torres Sandra Martínez-Mariño Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models Applied Sciences energy efficiency heat loss coefficient machine learning XGBoost MLP SVR |
| title | Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models |
| title_full | Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models |
| title_fullStr | Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models |
| title_full_unstemmed | Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models |
| title_short | Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models |
| title_sort | heat loss coefficient estimation applied to existing buildings through machine learning models |
| topic | energy efficiency heat loss coefficient machine learning XGBoost MLP SVR |
| url | https://www.mdpi.com/2076-3417/10/24/8968 |
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