Adaptive thermal load prediction in residential buildings using artificial neural networks

Author:

Fouladfar Mohammad HosseinORCID,Soppelsa Anton,Nagpal Himanshu,Fedrizzi Roberto,Franchini GiuseppeORCID

Funder

Horizon 2020

Publisher

Elsevier BV

Subject

Mechanics of Materials,Safety, Risk, Reliability and Quality,Building and Construction,Architecture,Civil and Structural Engineering

Reference64 articles.

1. Energy consumption in households.” https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Energy_consumption_in_households (accessed July. 3, 2023).

2. Building thermal load prediction using deep learning method considering time-shifting correlation in feature variables;Lv;J. Build. Eng.,2022

3. Fifth-generation district heating and cooling substations: demand response with artificial neural network-based model predictive control;Buffa;Energies,2020

4. Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC;Bünning;Appl. Energy,2022

5. Neural network-based model predictive control system for optimizing building automation and management systems of sports facilities;Elnour;Appl. Energy,2022

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