Digital Twin for HVAC Load and Energy Storage based on a Hybrid ML Model with CTA-2045 Controls Capability
Author:
Affiliation:
1. University of Kentucky,SPARK Lab,ECE Department,Lexington,KY,USA
Funder
National Science Foundation
DoEd
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9947299/9947300/09948141.pdf?arnumber=9948141
Reference11 articles.
1. The Application of Machine Learning for the Voltage and Reactive Power Control in Power Distribution Network
2. Sequence-to-sequence deep learning model for building energy consumption prediction with dynamic simulation modeling
3. Generalized Energy Storage Model-In-the-Loop Suitable for Energy Star and CTA-2045 Control Types
4. Co-simulation of Smart Grids and Homes including Ultra-fast HVAC Models with CTA-2045 Control and Consideration of Thermal Comfort
Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. A Dynamic Urban Digital Twin Integrating Longitudinal Thermal Imagery for Microclimate Studies;Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation;2023-11-15
2. Co-Simulation of Electric Power Distribution Systems and Buildings including Ultra-Fast HVAC Models and Optimal DER Control;Sustainability;2023-06-12
3. Co-simulation of Smart Grids and Homes including Ultra-fast HVAC Models with CTA-2045 Control and Consideration of Thermal Comfort;2022 IEEE Energy Conversion Congress and Exposition (ECCE);2022-10-09
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