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Hardware Design of Smart Home Energy Management System With Dynamic Price Response
Abstract
The smart grid initiative and electricity market operation drive the development known as demand-side management or controllable load. Home energy management has received increasing interest due to the significant amount of loads in the residential sector. This paper presents a hardware design of smart home energy management system (SHEMS) with the applications of communication, sensing technology, and machine learning algorithm. With the proposed design, consumers can easily achieve a real-time, price-responsive control strategy for residential home loads such as electrical water heater (EWH), heating, ventilation, and air conditioning (HVAC), electrical vehicle (EV), dishwasher, washing machine, and dryer. Also, consumers may interact with suppliers or load serving entities (LSEs) to facilitate the load management at the supplier side. Further, SHEMS is designed with sensors to detect human activities and then a machine learning algorithm is applied to intelligently help consumers reduce total payment on electricity without or with little consumer involvement. Finally, simulation and experiment results are presented based on an actual SHEMS prototype to verify the hardware system.
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“For this, automated demand-side response [357] can respond to electricity prices, smart meter signals, or learned user preferences [393].”
From Tackling climate change with machine learning (2023) · cites this paper as [357]
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Mushtaq, M. I., Siddiq, A., Tariq, M. O., Ali, A., & Zahra, A. (2019) · July 2019 · doi:10.22581/muet1982.1903.06 · Cited by 1
Chen, T., Hu, Q., Wang, Y., Quan, X., Zhang, F., & Yan, X. (2022) · International Conference on Power Energy Systems and Applications (ICoPESA) · doi:10.1109/icopesa54515.2022.9754456 · Cited by 2
Self-citationLife test design for retired xEV batteries aiming at smart home applications
Li, H., Alsolami, M., Xiao, Y., Dong, X., Zhu, K. M., & Wang, J. (2015) · IEEE Energy Conversion Congress and Exposition (ECCE) · doi:10.1109/ecce.2015.7310360 · Cited by 2
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Diefenderfer, P., & Jansson, P. M. (2014) · IEEE Sensors Applications Symposium (SAS) · doi:10.1109/sas.2014.6798933 · Cited by 2
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Metadata
- Authors
- Hu, Q., & Li, F.
- Affiliations
- University of Tennessee at Knoxville (US)
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2013-12
- Volume
- 4
- Issue
- 4
- Pages
- 1878-1887
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W1972262259
- Cited by
- 261
- References
- 18
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 17 · 2023: 9 · 2022: 13 · 2021: 22 · 2020: 28 · 2019: 26 · 2018: 30 · 2017: 30 · 2016: 28 · 2015: 30 · 2014: 14 · 2013: 1
- FWCI
- 14.60
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Smart Grid (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT-based Smart Home Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Keywords
- smart home energy management systems, demand-side management, flexible loads, electricity pricing, human activity sensing, load serving entities, smart grid, hardware prototype
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