IEEE Access · 2018 · 405 citations · 101 references
Artificial IntelligenceEngineeringEnergy EfficiencyLoad ControlIntelligent Energy SystemSystems EngineeringSmart EnergyEnergy ControlEnergy Demand ManagementElectrical EngineeringSmart BuildingEnergy System MonitoringComputer EngineeringSmart HomeSmart GridEnergy ManagementIntelligent ControllersDemand ResponseArtificial Neural NetworkSmart Technologies
The growing electricity demand and smart grid deployment create opportunities for home energy management systems to reduce consumption through demand response and smart meter‑enabled load control. This review surveys prior and current research on HEMS, focusing on demand response programs, smart technologies, and load‑scheduling controllers. HEMS employ demand‑response tools that shift and curtail load, generate optimal schedules based on cost, environmental, load, and comfort factors, and incorporate AI controllers such as neural networks, fuzzy logic, and adaptive neural fuzzy inference, as well as heuristic optimization methods.
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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