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, Enrique Granada-Álvarez
<span title="2021-08-22">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/a2yvk5xhdnhpxjnk6yd33uudqq" style="color: black;">Energies</a> </i> &nbsp;
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
more &raquo; ... 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/en14165188">doi:10.3390/en14165188</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lvgxcdrmgjdl3c763h5i5vhbne">fatcat:lvgxcdrmgjdl3c763h5i5vhbne</a> </span>
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