The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers
Open accessAbstract
The increasing demand for electricity and the emergence of smart grids have presented new opportunities for a home energy management system (HEMS) that can reduce energy usage. The HEMS incorporates a demand response (DR) tool that shifts and curtails demand to improve home energy consumption. This system commonly creates optimal consumption schedules by considering several factors, such as energy costs, environmental concerns, load profiles, and consumer comfort. With the deployment of smart meters, performing load control using the HEMS with DR-enabled appliances has become possible. This paper provides a comprehensive review on previous and current research related to the HEMS by considering various DR programs, smart technologies, and load scheduling controllers. The application of artificial intelligence for load scheduling controllers, such as artificial neural network, fuzzy logic, and adaptive neural fuzzy inference system, is also reviewed. Heuristic optimization techniques, which are widely used for optimal scheduling of various electrical devices in a smart home, are also discussed.
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Cites
Erdinc, O., Paterakis, N. G., Mendes, T. D. P., Bakirtzis, A. G., & P. S. Catalao, J. (2015) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2014.2352650 · Cited by 462
Smart home energy management system including renewable energy based on ZigBee and PLC
Han, J., Choi, C.-S., Park, W.-K., Lee, I., & Kim, S.-H. (2014) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2014.6851994 · Cited by 449 · cited 1×
“A ZigBee wireless sensor based on HEMS that monitors the energy consumption of residential loads and lights was also designed [73].”
Hardware Design of Smart Home Energy Management System With Dynamic Price Response
Hu, Q., & Li, F. (2013) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2013.2258181 · Cited by 261
An efficient power scheduling scheme for residential load management in smart homes
Rasheed, M., Javaid, N., Ahmad, A., Khan, Z., Qasim, U., & Alrajeh, N. (2015) · Applied Sciences · doi:10.3390/app5041134 · Cited by 134
Cited by
Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., Flynn, D., Elizondo-Gonzalez, S., & Wattam, S. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109899 · Cited by 656
Energy management system for smart grid: An overview and key issues
Rathor, S. K., & Saxena, D. (2020) · International Journal of Energy Research · doi:10.1002/er.4883 · Cited by 494
A Multi-Agent Reinforcement Learning-Based Data-Driven Method for Home Energy Management
Xu, X., Jia, Y., Xu, Y., Xu, Z., Chai, S., & Lai, C. S. (2020) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2020.2971427 · Cited by 420
Bakare, M. S., Abdulkarim, A., Zeeshan, M., & Shuaibu, A. N. (2023) · Energy Informatics · doi:10.1186/s42162-023-00262-7 · Cited by 283
Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment
Khan, M. A., Saleh, A. M., Waseem, M., & Sajjad, I. A. (2023) · IEEE Access · doi:10.1109/access.2022.3231444 · Cited by 179
Metadata
- Authors
- Shareef, H., Ahmed, M. S., Mohamed, A., & Al Hassan, E.
- Venue
- IEEE Access
- Published
- 2018
- Volume
- 6
- Pages
- 24498-24509
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2801424816
- Cited by
- 433
- References
- 4
- FWCI
- 17.31
- Type
- journal article
- Language
- en
- Open access
- gold
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