TY - JOUR
T1 - Digital twins for efficient modeling and control of buildings an integrated solution with scada systems
AU - Jain, Achin
AU - Nong, Derek
AU - Nghiem, Truong X.
AU - Mangharam, Rahul
N1 - Publisher Copyright:
© 2018 ASHRAE (www.ashrae.org) and IBPSA-USA (www.ibpsa.us).
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
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M3 - Conference article
AN - SCOPUS:85099257331
SN - 2574-6308
JO - ASHRAE and IBPSA-USA Building Simulation Conference
JF - ASHRAE and IBPSA-USA Building Simulation Conference
T2 - 2018 ASHRAE/IBPSA-USA Building Simulation Conference: Building Performance Modeling, SimBuild 2018
Y2 - 26 September 2018 through 28 September 2018
ER -