We found that MPC can reduce operating costs by 15.8 % and reduce the peak power demand by 24.8 % compared with rule-based storage-priority control. The MPC was formulated as a nonlinear programming problem and solved using a global optimization solver. Then the virtual system was randomly perturbed to generate training data for the MPC models. A virtual high-fidelity building testbed was created in Modelica based on actual measurement data from a chiller plant with an ice storage tank system. In this study, we estimate how MPC parameters such as the prediction horizon (PH) can influence building demand flexibility.
Different parameter settings of MPC have also been shown to have significant influence on building power usage, which may therefore influence building demand flexibility. Model Predictive Control (MPC) has been demonstrated to be an efficient way to reduce building operating costs, especially for buildings with thermal storage systems, by changing the power demand profiles. Finally, the importance of standardized performance assessment and methodology for comparison of different building control algorithms is discussed. The paper draws practical guidelines with a generic workflow for implementation of MPC in real buildings aimed for contemporary adopters of this technology. On top of this, the paper 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. From a practical point of view, this paper delivers an elaborate classification of the most important modeling, co-simulation, optimal control design, and optimization techniques, tools, and solvers suitable to tackle the MPC problems in the context of building climate control. The paper categorizes the most notable MPC problem classes, links them with corresponding solution techniques, and provides an overview of methods for mitigation of the uncertainties for increased performance and robustness of MPC. From a theoretical point of view, this paper presents an overview of MPC formulations for building control, modeling paradigms and model types, together with algorithms necessary for real-life implementation. This paper provides a unified framework for model predictive building control technology with focus on the real-world applications. There is a growing need for multidisciplinary education on advanced control methods in the built environment to be accessible for a broad range of researchers and practitioners with different engineering backgrounds. However, despite intensive research efforts, the practical applications are still in the early stages. If this data is unavailable or inaccurate and you own or represent this business, click here for more information on how you may be able to correct it.It has been proven that advanced building control, like model predictive control (MPC), can notably reduce the energy use and mitigate greenhouse gas emissions.
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