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Article

Estimation of Heat Loss Coefficient and Thermal Demands of In-Use Building by Capturing Thermal Inertia Using LSTM Neural Networks

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Department of Mechanical Engineering, Heat Engines and Fluids Mechanics, Industrial Engineering School, University of Vigo, Maxwell s/n, 36310 Vigo, Spain
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Defense University Center, Spanish Naval Academy, Plaza de España, s/n, 36920 Marín, Spain
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TENECO Research Group, Department of Mechanical Engineering, University of La Rioja, Calle San Jose de Calasanz, 31, 26004 Logroño, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Robert Černý
Energies 2021, 14(16), 5188; https://doi.org/10.3390/en14165188
Received: 20 July 2021 / Revised: 14 August 2021 / Accepted: 18 August 2021 / Published: 22 August 2021
Accurate forecasting of a building thermal performance can help to optimize its energy consumption. In addition, obtaining the Heat Loss Coefficient (HLC) allows characterizing the thermal envelope of the building under conditions of use. The aim of this work is to study the thermal inertia of a building developing a new methodology based on Long Short-Term Memory (LSTM) neural networks. This approach was applied to the Rectorate building of the University of Basque Country (UPV/EHU), located in the north of Spain. A comparison of different time-lags selected to catch the thermal inertia has been carried out using the CV(RMSE) and the MBE errors, as advised by ASHRAE. The main contribution of this work lies in the analysis of thermal inertia detection and its influence on the thermal behavior of the building, obtaining a model capable of predicting the thermal demand with an error between 12 and 21%. Moreover, the viability of LSTM neural networks to estimate the HLC of an in-use building with an error below 4% was demonstrated. View Full-Text
Keywords: building performance; HLC; LSTM; machine learning; thermal inertia building performance; HLC; LSTM; machine learning; thermal inertia
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MDPI and ACS Style

Pensado-Mariño, M.; Febrero-Garrido, L.; Pérez-Iribarren, E.; Oller, P.E.; Granada-Álvarez, E. Estimation of Heat Loss Coefficient and Thermal Demands of In-Use Building by Capturing Thermal Inertia Using LSTM Neural Networks. Energies 2021, 14, 5188. https://doi.org/10.3390/en14165188

AMA Style

Pensado-Mariño M, Febrero-Garrido L, Pérez-Iribarren E, Oller PE, Granada-Álvarez E. Estimation of Heat Loss Coefficient and Thermal Demands of In-Use Building by Capturing Thermal Inertia Using LSTM Neural Networks. Energies. 2021; 14(16):5188. https://doi.org/10.3390/en14165188

Chicago/Turabian Style

Pensado-Mariño, Martín, Lara Febrero-Garrido, Estibaliz Pérez-Iribarren, Pablo E. Oller, and Enrique Granada-Álvarez. 2021. "Estimation of Heat Loss Coefficient and Thermal Demands of In-Use Building by Capturing Thermal Inertia Using LSTM Neural Networks" Energies 14, no. 16: 5188. https://doi.org/10.3390/en14165188

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