Multi-Physics and Artificial Intelligence Models for Digital Twin Implementations of Residential Electric Loads

Steven B. Poore; Rosemary E. Alden; Huangjie Gong; Dan M. Ionel

doi:10.1109/ICRERA55966.2022.9922831

Multi-Physics and Artificial Intelligence Models for Digital Twin Implementations of Residential Electric Loads

Steven B. Poore, Rosemary E. Alden, Huangjie Gong, Dan M. Ionel

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

Abstract

Heating, ventilation, and air-conditioning (HVAC) and electric water heating (EWH) represent residential loads. Simulating these appliances for electric load forecasting, demand response (DR) studies, and human behavior analysis using physics-based models and artificial intelligence (AI) can further advance smart home technology. This paper explains the background of residential load modeling, starting with the concept of digital twin (DT) as well as the different types of methods. Two major types of appliance load monitoring (ALM) and their advantages/disadvantages are then discussed. This is followed by a review of multiple studies on residential load modeling, particularly for HVAC, EWH, and human behavior. Further examples of electric load forecasts and DR case studies using experimental smart homes are provided. The results and impact of these studies are discussed, as well as their contribution to the advancement of smart home technology and large-scale application.

Original language	English
Title of host publication	11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022
Pages	576-581
Number of pages	6
ISBN (Electronic)	9781665471404
DOIs	https://doi.org/10.1109/ICRERA55966.2022.9922831
State	Published - 2022
Event	11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022 - Istanbul, Turkey Duration: Sep 18 2022 → Sep 21 2022

Publication series

Name	11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022

Conference

Conference	11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022
Country/Territory	Turkey
City	Istanbul
Period	9/18/22 → 9/21/22

Bibliographical note

Funding Information:
In other works by our group, an encoder-decoder LSTM ML model for day-ahead forecasting of residential HVAC energy usage based on previous load and future weather inputs was created in [14]. Further research has been performed to separate the HVAC system load from LSTM total power forecasts, effectively reducing data collection costs as smart meters would be the only required monitoring equipment [15]. These papers use the SHINES Smart Home field demonstration with rooftop solar PV, which is managed by the Electric Power Research Institute (EPRI) and funded by the Department of Energy (DOE) [16].

Funding Information:
This paper is based upon work supported by the National Science Foundation (NSF) under Award No. 1936131, including an REU supplement, and under NSF Graduate Research Fellowship Grant No. 1839289. Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF.

Publisher Copyright:
© 2022 IEEE.

Keywords

appliance load monitoring (ALM)
artificial intelligence (AI)
electric load forecasting
electric water heater (EWH)
heating ventilation and air conditioning (HVAC)
machine learning (ML)
residential
smart grid
smart home

ASJC Scopus subject areas

Information Systems and Management
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICRERA55966.2022.9922831

Cite this

Poore, S. B., Alden, R. E., Gong, H., & Ionel, D. M. (2022). Multi-Physics and Artificial Intelligence Models for Digital Twin Implementations of Residential Electric Loads. In 11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022 (pp. 576-581). (11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022). https://doi.org/10.1109/ICRERA55966.2022.9922831

Poore, Steven B. ; Alden, Rosemary E. ; Gong, Huangjie et al. / Multi-Physics and Artificial Intelligence Models for Digital Twin Implementations of Residential Electric Loads. 11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022. 2022. pp. 576-581 (11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022).

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title = "Multi-Physics and Artificial Intelligence Models for Digital Twin Implementations of Residential Electric Loads",

abstract = "Heating, ventilation, and air-conditioning (HVAC) and electric water heating (EWH) represent residential loads. Simulating these appliances for electric load forecasting, demand response (DR) studies, and human behavior analysis using physics-based models and artificial intelligence (AI) can further advance smart home technology. This paper explains the background of residential load modeling, starting with the concept of digital twin (DT) as well as the different types of methods. Two major types of appliance load monitoring (ALM) and their advantages/disadvantages are then discussed. This is followed by a review of multiple studies on residential load modeling, particularly for HVAC, EWH, and human behavior. Further examples of electric load forecasts and DR case studies using experimental smart homes are provided. The results and impact of these studies are discussed, as well as their contribution to the advancement of smart home technology and large-scale application.",

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note = "Funding Information: In other works by our group, an encoder-decoder LSTM ML model for day-ahead forecasting of residential HVAC energy usage based on previous load and future weather inputs was created in [14]. Further research has been performed to separate the HVAC system load from LSTM total power forecasts, effectively reducing data collection costs as smart meters would be the only required monitoring equipment [15]. These papers use the SHINES Smart Home field demonstration with rooftop solar PV, which is managed by the Electric Power Research Institute (EPRI) and funded by the Department of Energy (DOE) [16]. Funding Information: This paper is based upon work supported by the National Science Foundation (NSF) under Award No. 1936131, including an REU supplement, and under NSF Graduate Research Fellowship Grant No. 1839289. Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF. Publisher Copyright: {\textcopyright} 2022 IEEE.; null ; Conference date: 18-09-2022 Through 21-09-2022",

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Poore, SB, Alden, RE, Gong, H & Ionel, DM 2022, Multi-Physics and Artificial Intelligence Models for Digital Twin Implementations of Residential Electric Loads. in 11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022. 11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022, pp. 576-581, 11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022, Istanbul, Turkey, 9/18/22. https://doi.org/10.1109/ICRERA55966.2022.9922831

Multi-Physics and Artificial Intelligence Models for Digital Twin Implementations of Residential Electric Loads. / Poore, Steven B.; Alden, Rosemary E.; Gong, Huangjie et al.

11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022. 2022. p. 576-581 (11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022).

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

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AU - Gong, Huangjie

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N1 - Funding Information: In other works by our group, an encoder-decoder LSTM ML model for day-ahead forecasting of residential HVAC energy usage based on previous load and future weather inputs was created in [14]. Further research has been performed to separate the HVAC system load from LSTM total power forecasts, effectively reducing data collection costs as smart meters would be the only required monitoring equipment [15]. These papers use the SHINES Smart Home field demonstration with rooftop solar PV, which is managed by the Electric Power Research Institute (EPRI) and funded by the Department of Energy (DOE) [16]. Funding Information: This paper is based upon work supported by the National Science Foundation (NSF) under Award No. 1936131, including an REU supplement, and under NSF Graduate Research Fellowship Grant No. 1839289. Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF. Publisher Copyright: © 2022 IEEE.

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N2 - Heating, ventilation, and air-conditioning (HVAC) and electric water heating (EWH) represent residential loads. Simulating these appliances for electric load forecasting, demand response (DR) studies, and human behavior analysis using physics-based models and artificial intelligence (AI) can further advance smart home technology. This paper explains the background of residential load modeling, starting with the concept of digital twin (DT) as well as the different types of methods. Two major types of appliance load monitoring (ALM) and their advantages/disadvantages are then discussed. This is followed by a review of multiple studies on residential load modeling, particularly for HVAC, EWH, and human behavior. Further examples of electric load forecasts and DR case studies using experimental smart homes are provided. The results and impact of these studies are discussed, as well as their contribution to the advancement of smart home technology and large-scale application.

AB - Heating, ventilation, and air-conditioning (HVAC) and electric water heating (EWH) represent residential loads. Simulating these appliances for electric load forecasting, demand response (DR) studies, and human behavior analysis using physics-based models and artificial intelligence (AI) can further advance smart home technology. This paper explains the background of residential load modeling, starting with the concept of digital twin (DT) as well as the different types of methods. Two major types of appliance load monitoring (ALM) and their advantages/disadvantages are then discussed. This is followed by a review of multiple studies on residential load modeling, particularly for HVAC, EWH, and human behavior. Further examples of electric load forecasts and DR case studies using experimental smart homes are provided. The results and impact of these studies are discussed, as well as their contribution to the advancement of smart home technology and large-scale application.

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KW - electric load forecasting

KW - electric water heater (EWH)

KW - heating ventilation and air conditioning (HVAC)

KW - machine learning (ML)

KW - residential

KW - smart grid

KW - smart home

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M3 - Conference contribution

AN - SCOPUS:85142096857

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Poore SB, Alden RE, Gong H, Ionel DM. Multi-Physics and Artificial Intelligence Models for Digital Twin Implementations of Residential Electric Loads. In 11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022. 2022. p. 576-581. (11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022). doi: 10.1109/ICRERA55966.2022.9922831

Multi-Physics and Artificial Intelligence Models for Digital Twin Implementations of Residential Electric Loads

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this