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

Pensado-Mariño, Martín; Febrero-Garrido, Lara; Pérez-Iribarren, Estibaliz; Oller, Pablo Eguía; Granada-Álvarez, Enrique

doi:10.3390/en14165188

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

Martín Pensado-Mariño

^1,*

Lara Febrero-Garrido

Estibaliz Pérez-Iribarren

³,

Pablo Eguía Oller

and

Enrique Granada-Álvarez

Department of Mechanical Engineering, Heat Engines and Fluids Mechanics, Industrial Engineering School, University of Vigo, Maxwell s/n, 36310 Vigo, Spain

Defense University Center, Spanish Naval Academy, Plaza de España, s/n, 36920 Marín, Spain

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

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Abstract

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

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

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