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 proceedingConference contributionpeer-review

2 Scopus citations

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 languageEnglish
Title of host publication11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022
Pages576-581
Number of pages6
ISBN (Electronic)9781665471404
DOIs
StatePublished - 2022
Event11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022 - Istanbul, Turkey
Duration: Sep 18 2022Sep 21 2022

Publication series

Name11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022

Conference

Conference11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022
Country/TerritoryTurkey
CityIstanbul
Period9/18/229/21/22

Bibliographical note

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

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