Abstract
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.
Highlights
Buildings are responsible for approximately 40% of the total energy consumption in industrialized countries (Cao et al 2016a) and they, present large opportunities and barriers (Ma et al 2016) for improving energy efficiency by advancing building intelligence (Jørgensen et al 2015) Buildings are part of complex ecosystems where the primary uses of the buildings are more important than their energy use
Analyzing the data of the CO2 levels versus the Variable air volume (VAV) damper setpoints applied by the Zone Control Application we see that the algorithm behaves reactively in cases where it observes that the CO2 level is rising beyond comfort levels
The Digital Twin Framework contains a parametrized Zone Model which can be calibrated against controllable zones as well as a parametrized Zone Control Application which enables the configuration of targets for temperature- and CO2
Summary
Buildings are responsible for approximately 40% of the total energy consumption in industrialized countries (Cao et al 2016a) and they, present large opportunities and barriers (Ma et al 2016) for improving energy efficiency by advancing building intelligence (Jørgensen et al 2015) Buildings are part of complex ecosystems where the primary uses of the buildings are more important than their energy use. This is for instance the case for commercial buildings like retail stores (Ma et al 2017a) and hospitals (Billanes et al 2018). Model Predictive Control (MPC) is well adapted to these new requirements (Bianchini et al 2016)
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