TY - JOUR
T1 - Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers
AU - Shareef, Hussain
AU - Ahmed, Maytham S.
AU - Mohamed, Azah
AU - Al Hassan, Eslam
N1 - Funding Information:
This work was supported in part by Universiti Kebangsaan Malaysia under Grant DIP-2014-028 and in part by United Arab Emirates University under Grant G00001888.
Publisher Copyright:
© 2013 IEEE.
PY - 2018/4/28
Y1 - 2018/4/28
N2 - 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.
AB - 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.
KW - Home energy management system
KW - demand response
KW - integrated wireless technology
KW - intelligent scheduling controller
KW - smart technologies
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U2 - 10.1109/ACCESS.2018.2831917
DO - 10.1109/ACCESS.2018.2831917
M3 - Article
AN - SCOPUS:85046372916
SN - 2169-3536
VL - 6
SP - 24498
EP - 24509
JO - IEEE Access
JF - IEEE Access
ER -