Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC-Reference-Cited by-同舟云学术

Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC

Published:2022-03 Issue: Volume:310 Page:118491
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Bünning Felix^ORCID,Huber Benjamin^ORCID,Schalbetter Adrian,Aboudonia Ahmed^ORCID,Hudoba de Badyn Mathias,Heer Philipp^ORCID,Smith Roy S.,Lygeros John^ORCID

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference79 articles.

1. Supporting energy efficiency progress in major economies;IEA Building Energy Performance Metrics,2015

2. All you need to know about model predictive control for buildings;Drgoňa;Annu Rev Control,2020

3. A review on optimized control systems for building energy and comfort management of smart sustainable buildings;Hameed Shaikh;Renew Sustain Energy Rev,2014

4. Model predictive control: Past, present and future;Morari;Comput Chem Eng,1999

5. Use of model predictive control and weather forecasts for energy efficient building climate control;Oldewurtel;Energy Build,2012

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