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

Martín Pensado-Mariño, Lara Febrero-Garrido, Estibaliz Pérez-Iribarren, Pablo Eguía Oller and Enrique Granada-Álvarez
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Martín Pensado-Mariño: Department of Mechanical Engineering, Heat Engines and Fluids Mechanics, Industrial Engineering School, University of Vigo, Maxwell s/n, 36310 Vigo, Spain
Lara Febrero-Garrido: Defense University Center, Spanish Naval Academy, Plaza de España, s/n, 36920 Marín, Spain
Estibaliz Pérez-Iribarren: TENECO Research Group, Department of Mechanical Engineering, University of La Rioja, Calle San Jose de Calasanz, 31, 26004 Logroño, Spain
Pablo Eguía Oller: Department of Mechanical Engineering, Heat Engines and Fluids Mechanics, Industrial Engineering School, University of Vigo, Maxwell s/n, 36310 Vigo, Spain
Enrique Granada-Álvarez: Department of Mechanical Engineering, Heat Engines and Fluids Mechanics, Industrial Engineering School, University of Vigo, Maxwell s/n, 36310 Vigo, Spain

Energies, 2021, vol. 14, issue 16, 1-14

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.

Keywords: building performance; HLC; LSTM; machine learning; thermal inertia (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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