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An Optimal Power Scheduling for Smart Home Appliances with Smart Battery using Grey Wolf Optimizer

机译:使用灰狼优化器的带有智能电池的智能家电优化功率调度

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In this paper, Grey Wolf optimizer (GWO) is adapted for Power Scheduling Problem (PSP) of Smart Home with Smart Battery. GWO is the recent metaheuristic swarm-based optimization method stemmed by grey pack behaviour in hunting process. It has been successfully tailored to a wide variety of real-world optimization problems. PSP is tackled by scheduling the home appliances over a certain time horizon to minimize both the electricity bill and the peak-to-average ratio (PAR) as well as to improve the users comfort level. A new formulation for smart battery (SB) and its impact to achieve the objectives are also utilized and considered as the main part of this paper. The simulation results prove the efficiency of SB in minimizing the electricity bill and PAR and improving the user comfort. Furthermore, GWO proves its efficiency in obtaining the best schedule results in comparison with genetic algorithm (GA) results. In conclusion, SB has a high impact in power scheduling of appliances for smart home.
机译:本文将灰狼优化器(GWO)应用于具有智能电池的智能家居的电源调度问题(PSP)。 GWO是最近的基于元启发式群的优化方法,该方法基于狩猎过程中的灰包行为。它已成功地针对各种现实世界中的优化问题进行了量身定制。通过在一定时间范围内调度家用电器来解决PSP问题,以最大程度地减少电费和峰均比(PAR)并提高用户的舒适度。还采用了一种新的智能电池配方(SB)及其对实现目标的影响,并将其视为本文的主要部分。仿真结果证明了SB在最小化电费和PAR并提高用户舒适度方面的效率。此外,与遗传算法(GA)结果相比,GWO证明了其获得最佳调度结果的效率。总之,SB在智能家居设备的电源调度中具有很大的影响。

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