A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
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 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%. ; Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-096296-B-C21
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 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%. ; Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-096296-B-C21
Heat loss coefficient estimation applied to existing buildings through machine learning models
Martínez Comesaña, Miguel (author) / Febrero Garrido, Lara (author) / Granada Álvarez, Enrique (author) / Martínez Torres, Javier (author) / Martinez Mariño, Sandra (author)
2020-12-16
Article (Journal)
Electronic Resource
English
DDC:
690
Heat loss coefficient estimation applied to existing buildings through machine learning models
| BASE | 2020
| BASE | 2020
| BASE | 2020
| BASE | 2020
HEAT LOSS COEFFICIENT ESTIMATION SYSTEM, HEAT LOSS COEFFICIENT ESTIMATION METHOD, AND PROGRAM
| European Patent Office | 2023