DIGITAL TWINS FOR EFFICIENT MODELING AND CONTROL OF BUILDINGS AN INTEGRATED SOLUTION WITH SCADA SYSTEMS

被引:0
|
作者
Jain, Achin [1 ]
Nong, Derek [1 ]
Nghiem, Truong X. [2 ]
Mangharam, Rahul [1 ]
机构
[1] Flexergy AI, Philadelphia, PA 19103 USA
[2] No Arizona Univ, Flagstaff, AZ 86011 USA
关键词
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中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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 van- 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 tram 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.
引用
收藏
页码:799 / 806
页数:8
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