Intelligent Algorithms in Home Energy Management Systems: A Survey

Liu, Qi; Dannah, Williams; Liu, Xiaodong

doi:10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00094

Cited by 2 publications

(1 citation statement)

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“…However, load shifting on the smart grids is done during peak hours to prevent peak load and damage to equipment. This is profitable for the company and the consumers and also increases network reliability [11]. Responding to such challenges requires meta-heuristic optimization algorithms that are highly capable of solving multi-objective optimization problems to manage energy consumption.…”

Section: Introductionmentioning

confidence: 99%

A new optimal energy management strategy based on improved multi-objective antlion optimization algorithm: applications in smart home

2020

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In recent years, energy demand has grown significantly relative to its production. The power companies have also offered a variety of schemes such as energy consumption management to meet this growing consumer demand. Energy consumption management is a set of strategies used to optimize energy consumption which includes a set of interconnected activities between the utility and customers to transfer the load from peak hours to off-peak hours. This reduces the electricity bill. This paper presents an optimal schedule for the consumption of residential appliances based on improved multi-objective antlion optimization algorithm to minimize the electrical cost and the user comfort. To prevent peaks, the peak-to-average ratio is considered as a constraint for the energy cost function. Also, two different tariff signals have been used to measure energy costs. The real-time pricing and critical peak pricing are considered as energy tariffs. The simulations results are compared with other meta-heuristic algorithms, including multi-objective particle swarm optimization, the second version of the non-dominated sorting genetic algorithm, and the basic antlion optimizer algorithm to show the superiority of the proposed algorithm. Final results show that using the proposed scheme reaches electricity bills less than 80%.

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Section: Introductionmentioning

confidence: 99%