Model Predictive Control (MPC) can be used in the context of building automation to improve energy efficiency and occupant comfort. Ideally, the MPC algorithm should consider current- and planned usage of the controlled environment along with initial state and weather forecast to plan for optimal comfort and energy efficiency. This implies the need for an MPC application which 1) considers multiple objectives, 2) can draw on multiple data sources, and 3) provides an approach to effectively integrate against heterogeneous building automation systems to make the approach reusable across different installations. To this end, this paper presents a design and implementation of a framework for digital twins for buildings in which the controlled environments are represented as digital entities. In this framework, digital twins constitute parametrized models which are integrated into a generic control algorithm that uses data on weather forecasts, current- and planned occupancy as well as the current state of the controlled environment to perform MPC. This data is accessed through a generic data layer to enable uniform data access. This enables the framework to switch seamlessly between simulation and real-life applications and reduces the barrier towards reusing the application in a different control environment. We demonstrate an application of the digital twin framework on a case study at the University of Southern Denmark where a digital twin has been used to control heating and ventilation. From the case study, we observe that we can switch from rule-based control to model predictive control with no immediate adverse effects towards comfort or energy consumption. We also identify the potential for an increase in energy efficiency, as well as introduce the possibility of planning energy consumption against local electricity production or market conditions, while maintaining occupant comfort.

中文翻译：

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用于提高公共和商业建筑能源效率和居住舒适度的数字孪生框架

模型预测控制 (MPC) 可用于楼宇自动化环境，以提高能源效率和住户舒适度。理想情况下，MPC 算法应考虑受控环境的当前和计划使用以及初始状态和天气预报，以规划最佳舒适度和能源效率。这意味着需要一个 MPC 应用程序，它 1) 考虑多个目标，2) 可以利用多个数据源，以及 3) 提供一种有效集成异构楼宇自动化系统的方法，使该方法可在不同的安装中重复使用。为此，本文提出了建筑数字孪生框架的设计和实现，其中受控环境表示为数字实体。在这个框架中，数字孪生构成了参数化模型，这些模型被集成到通用控制算法中，该算法使用有关天气预报、当前和计划入住率以及受控环境的当前状态的数据来执行 MPC。这些数据通过通用数据层访问，以实现统一的数据访问。这使框架能够在模拟和现实应用程序之间无缝切换，并减少在不同控制环境中重用应用程序的障碍。我们在南丹麦大学的案例研究中展示了数字孪生框架的应用，其中数字孪生已用于控制加热和通风。从案例研究来看，我们观察到，我们可以从基于规则的控制切换到模型预测控制，而不会对舒适度或能源消耗产生直接的不利影响。我们还确定了提高能源效率的潜力，并介绍了根据当地电力生产或市场条件规划能源消耗的可能性，同时保持住户舒适度。