- By:
- Lebakula, Viswadeep ; Zandi, Helia ; Tsybina, Evgeniya ; Winstead, Christopher; Kuruganti, Phani Teja V; Hill, Justin
- Page Number:
- 1218-1224
- Issue Number:
- NA
- Book Title:
- 2023 IEEE Energy Conversion Congress and Exposition (ECCE)
- Publication Date:
- January 4, 2024
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- IEEE Energy Conversion Congress and Exposition (ECCE)
- Conference Location:
- Knoxville, Tennessee, United States of America
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/ECCE53617.2023.10362041
Abstract
Heating, ventilation, and air conditioning (HVAC) systems account for the highest share of home energy consumption in the United States. Optimized HVAC control can provide thermal improved comfort to the occupants, improve energy efficiency, reduce energy cost, and support grid services. In this paper, we discuss a multi-agent and cloud-based software framework that has been deployed in occupied residential neighborhood. This system enables automatic data collection, learning, optimization, and dispatches signals to neighborhood devices. HVAC optimization is based on model predictive control (MPC). Since the operational performance of MPC depends on model forecasting accuracy, it is crucial to evaluate the model continuously and modify or retrain it as necessary. In this research, we developed an automated workflow to evaluate the performance of temperature and power forecasts based on measured data in the real world. This will provide researchers with a deeper understanding of the model and how it can be improved.
