Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers

By Hussain Shareef, Maytham S. Ahmed, Azah Mohamed, Eslam Al Hassan. IEEE Access, 2018

370 citations recorded by the source corpus.

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.

Subfields: Building and Construction, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/a93b3ab1c1f69ce3d50f588419509968eac5bfc0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. David Flynn
2. Xu Xu
3. Zhao Xu
4. Benoit Couraud
5. S. Elizondo-González
6. Sonam Norbu
7. Valentin Robu
8. Desen Kirli

In Electrical and Electronic Engineering

1. David Flynn
2. Xu Xu
3. Zhao Xu
4. Benoit Couraud
5. S. Elizondo-González
6. Sonam Norbu
7. Valentin Robu
8. Desen Kirli

Citations in the corpus

• Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review (2020, background): “[14] where the authors have reviewed literature that utilise AI techniques for the development of schedule controller in a home energy management system (HEMS) which incorporates a DR tool.”
• Designing Decentralized Markets for Distribution System Flexibility (2019, neutral): “[44].”
• A Multi-Agent Reinforcement Learning-Based Data-Driven Method for Home Energy Management (2020, background): “Such actions of energy consumption scheduling are also referred to as demand response (DR), which balances supply and demand by adjusting elastic loads [1], [2].”