RT Journal Article
T1 Estimation of heat loss coefficient and thermal demands of in-use building by capturing thermal inertia using LSTM neural networks
A1 Pensado Mariño, Martín
A1 Febrero Garrido, Lara
A1 Pérez-Iribarren, Estibaliz
A1 Eguía Oller, Pablo
A1 Granada Álvarez, Enrique
K1 3305.90 Transmisión de Calor en la Edificación
K1 3305 Tecnología de la Construcción
K1 3328.16 Transferencia de Calor
AB 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.
PB Energies
SN 19961073
YR 2021
FD 2021-08-22
LK http://hdl.handle.net/11093/2465
UL http://hdl.handle.net/11093/2465
LA eng
NO Energies, 14(16): 5188 (2021)
NO Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-096296-B-C21
DS Investigo
RD 03-oct-2023