Prediction of Building’s Thermal Performance Using LSTM and MLP Neural Networks
Accurate prediction of building indoor temperatures and thermal demand is of great help to control and optimize the energy performance of a building. However, building thermal inertia and lag lead to complex nonlinear systems is difficult to model. In this context, the application of artificial neur...
Main Authors: | Miguel Martínez Comesaña, Lara Febrero-Garrido, Francisco Troncoso-Pastoriza, Javier Martínez-Torres |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-10-01
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Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/10/21/7439 |
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