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White-Model Predictive Control for Balancing Energy Savings and Thermal Comfort

Author

Listed:
  • Byung-Ki Jeon

    (Department of Smart City Engineering, Inha University, Inha-ro 100, Incheon 22212, Korea)

  • Eui-Jong Kim

    (Department of Architectural Engineering, Inha University, Inha-ro 100, Incheon 22212, Korea)

Abstract
To save energy consumed by a building, utilizing optimal predictive control with model predictive control (MPC) makes the most of energy storage systems (ESSs) to reduce the electrical energy consumption of peak and heavy loads. This study evaluated MPC applicability in a multi-zone commercial building using the EnergyPlus model and conducted multi-objective optimization of thermal comfort and energy savings. As a result of the simulation, optimal ESS charging scenarios responded to the fluctuating electricity pricing system, and changing the peak load time reduced the electricity bill of the grid by 55% compared to the existing operating method. At the same time, room temperatures stayed within the thermal comfort range, and the Pareto curve showed a proper balance between energy saving and thermal comfort. Especially, the proposed method with a white model is applicable for MPC applications in commercial buildings, as it gave optimal solutions within the target time interval.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2345-:d:777865
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    References listed on IDEAS

    as
    1. Byung-ki Jeon & Eui-Jong Kim, 2020. "Next-Day Prediction of Hourly Solar Irradiance Using Local Weather Forecasts and LSTM Trained with Non-Local Data," Energies, MDPI, vol. 13(20), pages 1-16, October.
    2. Reynolds, Jonathan & Rezgui, Yacine & Kwan, Alan & Piriou, Solène, 2018. "A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control," Energy, Elsevier, vol. 151(C), pages 729-739.
    3. Pinto, Giuseppe & Deltetto, Davide & Capozzoli, Alfonso, 2021. "Data-driven district energy management with surrogate models and deep reinforcement learning," Applied Energy, Elsevier, vol. 304(C).
    4. Byung-Ki Jeon & Eui-Jong Kim, 2021. "LSTM-Based Model Predictive Control for Optimal Temperature Set-Point Planning," Sustainability, MDPI, vol. 13(2), pages 1-14, January.
    5. 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.
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    Cited by:

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