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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Automated demand-side response can respond to electricity prices, smart meter signals, or learned user preferences.
“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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Hu, J., & Bui, N. H. B. (2024) · Energies · doi:10.3390/en17092039 · Cited by 19 · see citing works
An approach to assess the responsive residential demand to financial incentives
Hu, Q., Fang, X. Y., Li, F. F., Xu, X., Chen, C., & Hu, H. (2015) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm.2015.7286623 · Cited by 19 · see citing works
Self-citationDevelopment of an Energy-Efficient Smart Socket Based on STM32F103
Ma, M., Huang, B., Wang, B., Chen, J., & Liao, L. (2018) · Applied Sciences · doi:10.3390/app8112276 · Cited by 13 · see citing works
Network Architecture and Authentication Scheme for LoRa 2.4 GHz Smart Homes
Kane, L., Liu, V., McKague, M., & Walker, G. R. (2022) · IEEE Access · doi:10.1109/access.2022.3203387 · Cited by 18 · see citing works
An Approach for Distribution Transformer Management With a Multiagent System
Khamphanchai, W., Pipattanasomporn, M., Kuzlu, M., Zhang, J., & Rahman, S. (2014) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2014.2380413 · Cited by 18 · see citing works
Home energy management based on Bayesian network considering resident convenience
Shoji, T., Hirohashi, W., Fujimoto, Y., & Hayashi, Y. (2014) · International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) · doi:10.1109/pmaps.2014.6960597 · Cited by 18 · see citing works
Agheb, S., Tan, X., Sun, B., & Tsang, D. H. K. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2787716 · Cited by 18 · see citing works
Singh, B., Ray, R., Bhadoriya, J. S., Kumar, A., & Gupta, A. R. (2024) · Electrical Engineering · doi:10.1007/s00202-024-02274-2 · Cited by 17 · see citing works
Smart home energy management systems in India: a socio-economic commitment towards a green future
George, T., & Selvakumar, A. I. (2024) · Discover Sustainability · doi:10.1007/s43621-024-00295-2 · Cited by 17 · see citing works
- 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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