Occupant-centric control (OCC) strategies rely on different algorithms to learn and predict occupants’ patterns and preferences, then utilize these predictions to optimize building operations. However, testing different OCC algorithms or fine-tuning their configurations in real buildings can be a lengthy process. To this end, we present a framework for testing OCCs in a simulation environment prior to field implementation. The proposed workflow entails using synthetic occupant behaviour models and simulating OCC strategies to learn their preferences. The goal is to enable quick comparison of different OCC configurations under various scenarios by modifying occupant behaviour assumptions, as well as climate and design parameters. For proof-of-concept, the proposed method was applied in a case-study to simulate OCCs for lighting and heating/cooling setpoint adjustments in a single office under various occupant types, as well as OCC settings and design configurations. Results demonstrated the benefits of the proposed framework and its potential for providing a more holistic evaluation of OCCs under different scenarios. Using the proposed framework, building designers and operators can identify potential issues with OCCs and fine-tune their settings prior to field implementation.

以乘员为中心的控制（OCC）策略依靠不同的算法来学习和预测乘员的模式和喜好，然后利用这些预测来优化建筑物的运行。但是，在实际建筑物中测试不同的OCC算法或对其配置进行微调可能是一个漫长的过程。为此，我们提出了在现场实施之前在模拟环境中测试OCC的框架。拟议的工作流程需要使用综合的乘员行为模型并模拟OCC策略以了解其偏好。目的是通过修改乘员的行为假设以及气候和设计参数，在各种情况下快速比较不同的OCC配置。对于概念验证，拟议的方法被应用于案例研究中，以模拟单个办公室在各种乘员类型，OCC设置和设计配置下用于照明和加热/制冷设定点调整的OCC。结果证明了拟议框架的好处及其在不同情况下对OCC进行更全面评估的潜力。使用提议的框架，建筑设计师和运营商可以在现场实施之前确定OCC的潜在问题并对其设置进行微调。