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- W4285103586 abstract "The time-of-use (TOU) tariff provides an opportunity for commercial buildings to reduce operational costs without compromising occupant comfort. Commercial buildings have daily thermostat setpoint schedules that can be easily adjusted at the beginning of the day to achieve such a goal. However, whether the zone temperature can be well kept during the day relies heavily on the accuracy of ambient temperature prediction. The robustness of optimized heating, ventilation, and air condition (HVAC) system schedules is critical to the satisfaction of occupants. This paper introduces a chance-constrained distributionally robust optimization (CCDRO) algorithm for daily HVAC scheduling that aims to improve the robustness of HVAC schedule against the uncertainty of ambient temperature. The proposed formulation is reformulated into a tractable form that can be solved efficiently using off-the-shelf optimization solvers. A case study using data from a real building is carried out to validate the feasibility of CCDRO. Numerical results indicate that CCDRO yields over 50% less temperature violation than the traditional stochastic optimization method with the same operational cost. The cost of achieving such performance is 15% lower than the traditional robust optimization method." @default.
- W4285103586 created "2022-07-14" @default.
- W4285103586 creator A5041796122 @default.
- W4285103586 creator A5048854154 @default.
- W4285103586 date "2022-04-24" @default.
- W4285103586 modified "2023-10-01" @default.
- W4285103586 title "Chance Constrained Distributionally Robust Optimal HVAC Scheduling for Commercial Building Demand Response" @default.
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- W4285103586 doi "https://doi.org/10.1109/isgt50606.2022.9817490" @default.
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