Building operation optimization (BOO) increasingly relies on diverse optimization paradigms. However, existing studies often employ inconsistent objectives, constraints, and modeling assumptions, making intrinsic trade-offs difficult to isolate. This study establishes a formulation-consistent benchmarking framework to compare black-box optimization (BBO) and mathematical programming optimization (MPO) for representative building energy systems. Two systems—an air-handling unit and a chiller plant derived from U.S. Department of Energy Commercial Prototype Buildings—are used as benchmark cases. Within a unified BOO formulation, BBO is implemented through iterative input–output evaluations using both high-fidelity EnergyPlus simulations and reduced-order models, whereas MPO employs explicit algebraic formulations solved using mathematical programming techniques. Representative gradient-based, mixed-integer, and population-based algorithms are evaluated under aligned objectives and constraints. The results show that all evaluated methods achieve comparable near-optimal energy performance. However, BBO requires approximately 10–100 times more computation time than MPO and scales less effectively as system complexity increases. Increasing model accuracy improves BBO performance primarily for the simpler air-handling-unit case, while providing limited benefit for the more complex chiller-plant case. These findings indicate that greater model fidelity does not necessarily lead to better optimization outcomes and that computational efficiency, system complexity, and model structure should be considered when selecting optimization methods for building operation.
