All you need to know about model predictive control for buildings
Ján Drgoňa,Ján Drgoňa,Javier Arroyo,Iago Cupeiro Figueroa,David Blum,Krzysztof Arendt,Donghun Kim,Donghun Kim,Enric Perarnau Ollé,Juraj Oravec,Michael Wetter,Draguna Vrabie,Lieve Helsen +12 more
TLDR
This paper provides a unified framework for model predictive building control technology with focus on the real-world applications and presents the essential components of a practical implementation of MPC such as different control architectures and nuances of communication infrastructures within supervisory control and data acquisition (SCADA) systems.About:
This article is published in Annual Reviews in Control.The article was published on 2020-01-01 and is currently open access. It has received 276 citations till now. The article focuses on the topics: Model predictive control.read more
Citations
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Dissertation
Robust model predictive control
TL;DR: This thesis studies the behaviour of the maximal robust positive invariant set for the case of scaled uncertainty and shows that this set is continuously polytopic up to a critical scaling factor, which can approximate a-priori with an arbitrary degree of accuracy.
Journal ArticleDOI
Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives
TL;DR: In this paper , a comprehensive overview of transfer learning applications in smart buildings, classifying and analyzing 77 papers according to their applications, algorithms, and adopted metrics, is presented, highlighting the role of deep learning in transfer learning in smart building applications.
Journal ArticleDOI
Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control
TL;DR: The approximate MPC is developed by learning from the measured operation data of buildings controlled by MPC, therefore it can produce MPC-like control for buildings without needing to solve the optimization problem, significantly reducing the computation load as compared to MPC.
Journal ArticleDOI
Physics-constrained deep learning of multi-zone building thermal dynamics
TL;DR: The proposed physics-constrained control-oriented deep learning method incorporates structural priors from traditional physics-based building modeling into the neural network thermal dynamics model structure, thereby bounding predictions within physically realistic and safe operating ranges.
Journal ArticleDOI
Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective
Sicheng Zhan,Adrian Chong +1 more
TL;DR: A finer categorization of past studies with respect to both modeling methods and modeling purposes is clarified and an extended Level of Detail framework is proposed to quantify the data usage in each study to bridge the gaps between data requirements, model performance, and control performance.
References
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Book
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TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.