Estimation of Heat Loss Coefficient and Thermal Demands of In-Use Building by Capturing Thermal Inertia Using LSTM Neural Networks
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 bui...
Main Authors: | , , , , |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-08-01
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Series: | Energies |
Subjects: | |
Online Access: | https://www.mdpi.com/1996-1073/14/16/5188 |