Digital Twin for HVAC Load and Energy Storage based on a Hybrid ML Model with CTA-2045 Controls Capability

Alden, Rosemary E.; Jones, Evan S.; Poore, Steven B.; Gong, Huangjie; Hadi, Abdullah Al; Ionel, Dan M.

doi:10.1109/ecce50734.2022.9948141

Cited by 4 publications

(1 citation statement)

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“…With these synthesized data, machine learning (ML) procedures may be applied to develop physics-informed new black and grey box versions that emulate the EnergyPlus and experimental data. Example methods used in the simulations throughout this paper include a hybrid ML model of k-means clustering to identify weather groupings, multiple linear regression (MLR), and specific heat conversions through thermodynamic equations as visualized in Figure 2 [26]. These methods may be updated in the object-oriented cosimulation framework as further improved methods are proposed.…”

Section: Models For Pv Generation and Energy Storagementioning

confidence: 99%

Co-Simulation of Electric Power Distribution Systems and Buildings including Ultra-Fast HVAC Models and Optimal DER Control

Jones

Alden

Gong³

et al. 2023

Sustainability

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Smart homes and virtual power plant (VPP) controls are growing fields of research with potential for improved electric power grid operation. A novel testbed for the co-simulation of electric power distribution systems and distributed energy resources (DERs) is employed to evaluate VPP scenarios and propose an optimization procedure. DERs of specific interest include behind-the-meter (BTM) solar photovoltaic (PV) systems as well as heating, ventilation, and air-conditioning (HVAC) systems. The simulation of HVAC systems is enabled by a machine learning procedure that produces ultra-fast models for electric power and indoor temperature of associated buildings that are up to 133 times faster than typical white-box implementations. Hundreds of these models, each with different properties, are randomly populated into a modified IEEE 123-bus test system to represent a typical U.S. community. Advanced VPP controls are developed based on the Consumer Technology Association (CTA) 2045 standard to leverage HVAC systems as generalized energy storage (GES) such that BTM solar PV is better utilized locally and occurrences of distribution system power peaks are reduced, while also maintaining occupant thermal comfort. An optimization is performed to determine the best control settings for targeted peak power and total daily energy increase minimization with example peak load reductions of 25+%.

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Section: Models For Pv Generation and Energy Storagementioning

confidence: 99%