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

Byung-Ki Jeon and Eui-Jong Kim
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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

Energies, 2022, vol. 15, issue 7, 1-12

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.

Keywords: model predictive control; multi-objective optimization; genetic algorithm; thermal comfort; energy saving (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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