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Multi-objective optimization for thermal mass model predictive control in small and medium size commercial buildings under summer weather conditions

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  • Li, Xiwang
  • Malkawi, Ali

Abstract

Building thermal mass control has great potentials in saving energy consumption and cost. Optimal control schemes are able to utilize passive thermal mass storage to shift the cooling load from peak hours to off-peak hours to reduce energy costs. As such, this paper explores the idea of model predictive control for building thermal mass control. Specifically, this paper presents a study of developing and evaluating a multi-objective optimization based model predictive control framework for demand response oriented building thermal mass control. This multi-objective optimization framework takes both energy cost and thermal comfort into consideration simultaneously. In this study, the developed model predictive control framework has been applied in six commercial buildings at Boston, Chicago, and Miami, under typical summer weather conditions. Time-of-use electricity prices from these three locations are used to calculate the cooling and reheating energy costs. Pareto curves for optimal temperature setpoints under different thermal comfort requirements are calculated to show the trade-off between the cost saving and thermal comfort maintaining. Comparing with a typical “night setback” operation scheme, this model predictive control schemes are able to save energy costs from 20% to 60% at these three locations under different weather and energy pricing conditions. In addition, the Pareto curves also show that the energy cost saving potentials are highly dependent on the thermal comfort requirements, weather conditions, utility rate structures, and the building constructions.

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  • Li, Xiwang & Malkawi, Ali, 2016. "Multi-objective optimization for thermal mass model predictive control in small and medium size commercial buildings under summer weather conditions," Energy, Elsevier, vol. 112(C), pages 1194-1206.
    • Handle: RePEc:eee:energy:v:112:y:2016:i:c:p:1194-1206
    DOI: 10.1016/j.energy.2016.07.021
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    11. Baghaee, H.R. & Mirsalim, M. & Gharehpetian, G.B. & Talebi, H.A., 2016. "Reliability/cost-based multi-objective Pareto optimal design of stand-alone wind/PV/FC generation microgrid system," Energy, Elsevier, vol. 115(P1), pages 1022-1041.
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    13. Schmidt, Mischa & Åhlund, Christer, 2018. "Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 742-756.
    14. Germán Ramos Ruiz & Eva Lucas Segarra & Carlos Fernández Bandera, 2018. "Model Predictive Control Optimization via Genetic Algorithm Using a Detailed Building Energy Model," Energies, MDPI, vol. 12(1), pages 1-18, December.
    15. Wang, Junke & Yik Tang, Choon & Song, Li, 2022. "Analysis of precooling optimization for residential buildings," Applied Energy, Elsevier, vol. 323(C).
    16. Galatioto, A. & Ciulla, G. & Ricciu, R., 2017. "An overview of energy retrofit actions feasibility on Italian historical buildings," Energy, Elsevier, vol. 137(C), pages 991-1000.
    17. Yuchun Li & Yinghua Han & Jinkuan Wang & Qiang Zhao, 2018. "A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort," Energies, MDPI, vol. 11(12), pages 1-20, December.
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    19. Byung-Ki Jeon & Eui-Jong Kim, 2022. "White-Model Predictive Control for Balancing Energy Savings and Thermal Comfort," Energies, MDPI, vol. 15(7), pages 1-12, March.
    20. Niu, Jide & Tian, Zhe & Lu, Yakai & Zhao, Hongfang, 2019. "Flexible dispatch of a building energy system using building thermal storage and battery energy storage," Applied Energy, Elsevier, vol. 243(C), pages 274-287.
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    22. Mostavi, Ehsan & Asadi, Somayeh & Boussaa, Djamel, 2017. "Development of a new methodology to optimize building life cycle cost, environmental impacts, and occupant satisfaction," Energy, Elsevier, vol. 121(C), pages 606-615.

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