Predictive pre-cooling of thermo-active building systems with low-lift chillers

Abstract This article describes the development and experimental validation of a data-driven model predictive control algorithm that optimizes the operation of a low-lift chiller, a variable-capacity chiller run at low pressure ratios, serving a single zone with a thermo-active building system. The predictive control algorithm incorporates new elements lacking in previous chiller pre-cooling control optimization methods, including a model of temperature and load-dependent chiller performance extending to low-pressure and part-load ratios and a data-driven zone temperature response model that accounts for the transient thermal response of a concrete-core radiant floor thermo-active building system. Data-driven models of zone and concrete-core thermal response are identified from monitored zone temperature and thermal load data and combined with an empirical model of a low-lift chiller to implement model predictive control. The energy consumption of the cooling system, including the chiller compressor, condenser fan, and chilled-water pump energy, is minimized over a 24-h look-ahead moving horizon using the thermo-active building system for thermal storage and radiant distribution. A generalized pattern-search optimization over compressor speed is performed to identify optimal chiller control schedules at every hour, thereby accomplishing load shifting, efficient part-load operation, and cooling energy savings. Results from testing the system's sensible cooling efficiency in an experimental test chamber subject to the typical summer week of two climates, Atlanta, GA, and Phoenix, AZ, show sensible cooling energy savings of 25% and 19%, respectively, relative to a high efficiency, variable-speed split-system air conditioner. Acknowledgments The authors wish to acknowledge the Masdar Institute of Science and Technology for support of this research. They are grateful for the support and advice of members of the Mitsubishi Electric Research Laboratory and the Pacific Northwest National Laboratory. Nicholas Gayeski is also thankful for the support of the Martin Family Society of Fellows for Sustainability. Thanks to Heejin Cho and Pacific Northwest National Laboratory for providing the intermediate files needed to conduct the modeled savings comparison and to Kurt Keville and the MIT Solar Decathlon team for the split system. Nicholas T. Gayeski, PhD, Associate Member ASHRAE, is Research Affiliate at Massachusetts Institute of Technology and Partner at KGS Buildings. Peter R. Armstrong, PhD, Member ASHRAE, is Associate Professor. Leslie K. Norford, PhD, Member ASHRAE, is Professor.