A Simulation-Based Optimization Method for Scheduling of Building Energy Management

Buildings account for substantial global energy consumption, with heating, ventilation, and air conditioning (HVAC) systems as major contributors. We study the setpoint schedule optimization of HVAC systems that minimize both energy costs and occupant discomfort. Since building performance simulation (BPS) tools provide high-fidelity models of building dynamics, integrating simulation with optimization is expected to obtain an effective schedule for building energy management. Consequently, many simulation-based optimization methods that integrate BPS into optimization processes are proposed. However, these methods still face challenges due to non-analytical system dynamics, computational complexity, and the lack of theoretical convergence guarantees. To address these challenges, a Lagrangian relaxation-based simulation optimization (LRSO) method is developed in this paper. A dynamic linear surrogate model iteratively refines itself with simulation outputs, balancing tractability and accuracy. Within Lagrangian relaxation framework, the problem is decomposed into simulation and optimization subproblems, which can be solved in a coordinated and decomposed way. The surrogate subgradient method further ensures the convergence. Experimental results demonstrate its superior performance in minimizing energy cost and occupant discomfort across all test scenarios, with computational times suitable for real-time scheduling. Note to Practitioners—Efficient scheduling of HVAC systems is critical for reducing building energy consumption while maintaining occupant comfort. However, the integration of optimization algorithms with simulation software remains challenging due to computational complexity, model incompatibility, and theoretical convergence insufficiency. This paper presents a practical solution through a Lagrangian relaxation-based simulation optimization (LRSO) method, which employs a dynamic surrogate model to approximate complex simulations and decomposes the problem into independently solvable subproblems. The method ensures convergence using a surrogate subgradient method and both the accuracy of the simulation software and the optimality of the optimization algorithm. Experimental results demonstrate that the LRSO method outperforms all other methods across all scenarios in reducing energy cost and occupant discomfort, while reducing computational overhead. In practice deployment, the proposed LRSO method can be embedded into Building Energy Management Systems (BEMS) as an optimizer software module. It receives real-time state from the sensor and future predictions, then provides the optimized solution strategy as a setpoint schedule for the actuator to control the corresponding HVAC devices. Furthermore, as its computational efficiency allows problem instances of practical scale to be solved within single time-step intervals under limited resources, the LRSO method can be applied to real-time rolling optimization like a model predictive controller, beyond the day-ahead scheduling. This approach enables building energy managers to develop reliable, high-performance HVAC schedules using existing simulation tools such as EnergyPlus, even under constrained computational resources.