We develop an integrated solution for incorporating “digi- tal twins” of real buildings into existing SCADA systems, which enables real-time prediction and advanced control. These digital twins are either EnergyPlus (E+) or data- driven (D+) building models, whose input and output vari- ables are mapped to analogous real building OPC tags and track the real-time operation of the building. An E+ dig- ital twin can be used to provide predictions of the build- ing's performance in different weather, usage, and energy pricing scenarios, which allows for accurate assessment of different control strategies. However, it is not suitable for optimization and predictive control due to its complexity. We develop scalable D+ digital twin based on Gaussian Processes (GP) for accurate prediction and advanced con- trol. A D+ digital twin is much easier, faster, and less ex- pensive to train than developing and tuning an E+ model, while still providing accurate power forecasts and being suitable for control. Data-driven Model Predictive Con- trol (MPC) optimizes control inputs of the predictive D+ model for energy curtailment with thermal comfort guar- antees in demand response applications. The MPC con- troller is integrated into the SCADA environment, demon- strating real-time in-the-loop control of D+ digital twins.
|Original language||English (US)|
|Journal||ASHRAE and IBPSA-USA Building Simulation Conference|
|State||Published - 2018|
|Event||2018 ASHRAE/IBPSA-USA Building Simulation Conference: Building Performance Modeling, SimBuild 2018 - Chicago, United States|
Duration: Sep 26 2018 → Sep 28 2018
ASJC Scopus subject areas
- Modeling and Simulation