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