Open AccessEvaluation of linear and nonlinear system models in hierarchical model predictive control of HVAC systems
Author(s) -
Steffen Eser,
Phillip Stoffel,
Alexander Kümpel,
Dirk Müller
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2042/1/012032
Subject(s) - model predictive control , hvac , controller (irrigation) , energy consumption , control theory (sociology) , modular design , computer science , linear model , nonlinear system , process (computing) , work (physics) , constraint (computer aided design) , mathematical optimization , constraint satisfaction , energy (signal processing) , control engineering , control (management) , engineering , mathematics , air conditioning , artificial intelligence , mechanical engineering , agronomy , statistics , physics , electrical engineering , quantum mechanics , machine learning , probabilistic logic , biology , operating system
Buildings are responsible for one third of the global final energy consumption. Model predictive control (MPC) can reduce their energy consumption and improve thermal comfort. However, designing the required models can be time consuming. Splitting the control problem into smaller subproblems could make the modeling process more modular and therefore cheaper. A hierarchical MPC structure is proposed in this work, where the building model is divided into a lower layer consisting of the producer side and an upper layer consisting of the consumers. Linear and non-linear model equations as well as a cost-based and a control quality-based cost function for a building energy system are developed. In a simulation, the nonlinear controller outperforms the linear controller in both constraint satisfaction and energy costs.