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Model Predictive Control for the Operation of Building Cooling Systems
Abstract
This brief presents a model-based predictive control (MPC) approach to building cooling systems with thermal energy storage. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. First, simplified models of chillers, cooling towers, thermal storage tanks, and buildings are developed and validated for the purpose of model-based control design. Then an MPC for the chilling system operation is proposed to optimally store the thermal energy in the tank by using predictive knowledge of building loads and weather conditions. This brief addresses real-time implementation and feasibility issues of the MPC scheme by using a simplified hybrid model of the system, a periodic robust invariant set as terminal constraints, and a moving window blocking strategy. The controller is experimentally validated at the University of California, Merced. The experiments show a reduction in the central plant electricity cost and an improvement of its efficiency.
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Cites
Constrained model predictive control: Stability and optimality
Mayne, D. Q., Rawlings, J. B., Rao, C. V., & Scokaert, P. O. M. (2000) · Automatica · doi:10.1016/s0005-1098(99)00214-9 · Cited by 8,736
Cited by
All you need to know about model predictive control for buildings
Drgoňa, J., Arroyo, J., Cupeiro Figueroa, I., Blum, D., Arendt, K., Kim, D., Ollé, E. P., Oravec, J., Wetter, M., Vrabie, D. L., et al. (2020) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2020.09.001 · Cited by 1,020
Serale, G., Fiorentini, M., Capozzoli, A., Bernardini, D., & Bemporad, A. (2018) · Energies · doi:10.3390/en11030631 · Cited by 637
An Introduction to Trajectory Optimization: How to Do Your Own Direct Collocation
Kelly, M. (2017) · SIAM Review · doi:10.1137/16m1062569 · Cited by 561
Deep Reinforcement Learning for Building HVAC Control
Wei, T., Wang, Y., & Zhu, Q. (2017) · Proceedings of the 54th Annual Design Automation Conference 2017 · doi:10.1145/3061639.3062224 · Cited by 478
Korkas, C. D., Baldi, S., Michailidis, I., & Kosmatopoulos, E. B. (2016) · Applied Energy · doi:10.1016/j.apenergy.2015.10.140 · Cited by 361
Metadata
- Authors
- Ma, Y., Borrelli, F., Hencey, B., Coffey, B., Bengea, S., & Haves, P.
- Venue
- IEEE Transactions on Control Systems Technology
- Published
- 2012-05
- Volume
- 20
- Issue
- 3
- Pages
- 796-803
- ISSN
- 1063-6536 (ISSN-L)
- 1558-0865
- OpenAlex
- W2019348145
- Cited by
- 527
- References
- 1
- FWCI
- 82.39
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ma, Y., Borrelli, F., Hencey, B., Coffey, B., Bengea, S., & Haves, P. (2012). Model Predictive Control for the Operation of Building Cooling Systems. IEEE Transactions on Control Systems Technology. https://doi.org/10.1109/tcst.2011.2124461
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
