Analysis of Building Model Forecasts using Autonomous HVAC Optimization System for Residential Neighborhood

Viswadeep Lebakula; Helia Zandi; Christopher Winstead; Eve Tsybina; Teja Kuruganti; Justin Hill

doi:10.1109/ECCE53617.2023.10362041

Analysis of Building Model Forecasts using Autonomous HVAC Optimization System for Residential Neighborhood

Viswadeep Lebakula, Helia Zandi, Christopher Winstead, Eve Tsybina, Teja Kuruganti, Justin Hill

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

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.

Original language	English
Title of host publication	2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1218-1224
Number of pages	7
ISBN (Electronic)	9798350316445
DOIs	https://doi.org/10.1109/ECCE53617.2023.10362041
State	Published - 2023
Event	2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023 - Nashville, United States Duration: Oct 29 2023 → Nov 2 2023

Publication series

Name	2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

Conference

Conference	2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023
Country/Territory	United States
City	Nashville
Period	10/29/23 → 11/2/23

Funding

This material is based upon work supported by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Buildings Technologies Office, under contract number DE-AC05-00OR22725. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

HVAC
MPC
docker
residential neighborhood
zone level analysis

Access to Document

10.1109/ECCE53617.2023.10362041

Cite this

Lebakula, V., Zandi, H., Winstead, C., Tsybina, E., Kuruganti, T., & Hill, J. (2023). Analysis of Building Model Forecasts using Autonomous HVAC Optimization System for Residential Neighborhood. In 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023 (pp. 1218-1224). (2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ECCE53617.2023.10362041

Lebakula, Viswadeep ; Zandi, Helia ; Winstead, Christopher et al. / Analysis of Building Model Forecasts using Autonomous HVAC Optimization System for Residential Neighborhood. 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1218-1224 (2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023).

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title = "Analysis of Building Model Forecasts using Autonomous HVAC Optimization System for Residential Neighborhood",

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.",

keywords = "HVAC, MPC, docker, residential neighborhood, zone level analysis",

author = "Viswadeep Lebakula and Helia Zandi and Christopher Winstead and Eve Tsybina and Teja Kuruganti and Justin Hill",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023 ; Conference date: 29-10-2023 Through 02-11-2023",

year = "2023",

doi = "10.1109/ECCE53617.2023.10362041",

language = "English",

series = "2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Lebakula, V , Zandi, H , Winstead, C , Tsybina, E , Kuruganti, T & Hill, J 2023, Analysis of Building Model Forecasts using Autonomous HVAC Optimization System for Residential Neighborhood. in 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023. 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023, Institute of Electrical and Electronics Engineers Inc., pp. 1218-1224, 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023, Nashville, United States, 10/29/23. https://doi.org/10.1109/ECCE53617.2023.10362041

Analysis of Building Model Forecasts using Autonomous HVAC Optimization System for Residential Neighborhood. / Lebakula, Viswadeep ; Zandi, Helia ; Winstead, Christopher et al.
2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1218-1224 (2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Kuruganti, Teja

AU - Hill, Justin

PY - 2023

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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.

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KW - MPC

KW - docker

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KW - zone level analysis

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ER -

Lebakula V , Zandi H , Winstead C , Tsybina E , Kuruganti T, Hill J. Analysis of Building Model Forecasts using Autonomous HVAC Optimization System for Residential Neighborhood. In 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1218-1224. (2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023). doi: 10.1109/ECCE53617.2023.10362041

Analysis of Building Model Forecasts using Autonomous HVAC Optimization System for Residential Neighborhood

Abstract

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