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Energy saving and indoor temperature control for an office building using tube-based robust model predictive control

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  • Gao, Yuan
  • Miyata, Shohei
  • Akashi, Yasunori

Abstract

Actively controlling a building’s heating, ventilation, and air conditioning (HVAC) system can reduce costs and improve indoor comfort. Model predictive control (MPC) is an effective control algorithm that can facilitate the active control of complex systems such as the HVAC system. However, the uncertainty of the prediction model engenders many challenges in practical application. To address these issues, we propose a tube-based MPC strategy. First, a reduced-order thermal capacitance and thermal resistance model is established for the target system. Subsequently, a tube-based MPC scheme is designed to effectively handle uncertainties in real systems. The prediction uncertainty space is re-assumed in the tube, combined with the actual prediction error, to more closely correspond to the actual situation. The proposed model is tested and validated using the BOPTEST open-source testing framework. The results show that the proposed tube-based MPC can reduce the operating cost by at least 24%, compared with the traditional open-loop and closed-loop MPC, and can better control the indoor temperature when considering multiple uncertain predictions.

Suggested Citation

  • Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2023. "Energy saving and indoor temperature control for an office building using tube-based robust model predictive control," Applied Energy, Elsevier, vol. 341(C).
    • Handle: RePEc:eee:appene:v:341:y:2023:i:c:s0306261923004701
    DOI: 10.1016/j.apenergy.2023.121106
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    References listed on IDEAS

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    1. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Multi-agent reinforcement learning dealing with hybrid action spaces: A case study for off-grid oriented renewable building energy system," Applied Energy, Elsevier, vol. 326(C).
    2. Li, Bingxu & Wu, Bingjie & Peng, Yelun & Cai, Wenjian, 2022. "Tube-based robust model predictive control of multi-zone demand-controlled ventilation systems for energy saving and indoor air quality," Applied Energy, Elsevier, vol. 307(C).
    3. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Operational optimization for off-grid renewable building energy system using deep reinforcement learning," Applied Energy, Elsevier, vol. 325(C).
    4. Shrivastava, R.L. & Vinod Kumar, & Untawale, S.P., 2017. "Modeling and simulation of solar water heater: A TRNSYS perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 126-143.
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    6. Arroyo, Javier & Manna, Carlo & Spiessens, Fred & Helsen, Lieve, 2022. "Reinforced model predictive control (RL-MPC) for building energy management," Applied Energy, Elsevier, vol. 309(C).
    7. Aste, Niccolò & Manfren, Massimiliano & Marenzi, Giorgia, 2017. "Building Automation and Control Systems and performance optimization: A framework for analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 313-330.
    8. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "Modeling, air balancing and optimal pressure set-point selection for the ventilation system with minimized energy consumption," Applied Energy, Elsevier, vol. 236(C), pages 574-589.
    9. Liu, Mingzhe & Ooka, Ryozo & Choi, Wonjun & Ikeda, Shintaro, 2019. "Experimental and numerical investigation of energy saving potential of centralized and decentralized pumping systems," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    10. Blum, David & Wang, Zhe & Weyandt, Chris & Kim, Donghun & Wetter, Michael & Hong, Tianzhen & Piette, Mary Ann, 2022. "Field demonstration and implementation analysis of model predictive control in an office HVAC system," Applied Energy, Elsevier, vol. 318(C).
    11. Thilker, Christian Ankerstjerne & Madsen, Henrik & Jørgensen, John Bagterp, 2021. "Advanced forecasting and disturbance modelling for model predictive control of smart energy systems," Applied Energy, Elsevier, vol. 292(C).
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    Cited by:

    1. Hu, Guoqing & You, Fengqi, 2023. "An AI framework integrating physics-informed neural network with predictive control for energy-efficient food production in the built environment," Applied Energy, Elsevier, vol. 348(C).
    2. Kamal Rsetam & Mohammad Al-Rawi & Ahmed M. Al-Jumaily & Zhenwei Cao, 2023. "Finite Time Disturbance Observer Based on Air Conditioning System Control Scheme," Energies, MDPI, vol. 16(14), pages 1-28, July.
    3. Nicola Lolli & Evgenia Gorantonaki & John Clauß, 2024. "Predictive Heating Control and Perceived Thermal Comfort in a Norwegian Office Building," Energies, MDPI, vol. 17(15), pages 1-23, July.
    4. Xu, Wenjie & Svetozarevic, Bratislav & Di Natale, Loris & Heer, Philipp & Jones, Colin N., 2024. "Data-driven adaptive building thermal controller tuning with constraints: A primal–dual contextual Bayesian optimization approach," Applied Energy, Elsevier, vol. 358(C).

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