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

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...

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Bibliographic Details
Main Authors: Martín Pensado-Mariño, Lara Febrero-Garrido, Estibaliz Pérez-Iribarren, Pablo Eguía Oller, Enrique Granada-Álvarez
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Energies
Subjects:
building performance
HLC
LSTM
machine learning
thermal inertia
Online Access:https://www.mdpi.com/1996-1073/14/16/5188

Internet

https://www.mdpi.com/1996-1073/14/16/5188

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