Adaptive Weight Building Energy Optimization: Thermal Comfort Flexibility
Kashani Lotfabadi, Alireza
Kashani Lotfabadi, Alireza
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Abstract
This study proposes a self-regulating adaptive optimization strategy, that leverages thermal-comfort-based energy flexibility to improve residential building heating system performance. Conventional thermostats rely on simple on/off control, while most model predictive control (MPC) approaches use fixed objective weights in multi-objective optimization. These methods cannot fully capture the time-varying tradeoffs among thermal comfort, economic costs, and environmental emissions. To address this limitation, a variable-weight optimization framework is employed, in which objective weights are dynamically adjusted. Based on consumer preferences, the model identifies a reference point in the objective space, from which the closest Pareto-optimal operating point is selected. Results show that the adaptive control framework regulates heating system in a manner that more closely aligns system performance with occupant preferences than both the static thermostat control and fixed-weight optimization.
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Date
1/1/2026
Student Status
Graduate Student
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Poster
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Program/Major
Mechanical Engineering
College/School
College of Engineering and Mathematical Sciences
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Engineering