TY - GEN
T1 - Analysis of Building Model Forecasts using Autonomous HVAC Optimization System for Residential Neighborhood
AU - Lebakula, Viswadeep
AU - Zandi, Helia
AU - Winstead, Christopher
AU - Tsybina, Eve
AU - Kuruganti, Teja
AU - Hill, Justin
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - HVAC
KW - MPC
KW - docker
KW - residential neighborhood
KW - zone level analysis
UR - http://www.scopus.com/inward/record.url?scp=85180012534&partnerID=8YFLogxK
U2 - 10.1109/ECCE53617.2023.10362041
DO - 10.1109/ECCE53617.2023.10362041
M3 - Conference contribution
AN - SCOPUS:85180012534
T3 - 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023
SP - 1218
EP - 1224
BT - 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023
Y2 - 29 October 2023 through 2 November 2023
ER -