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Optimal residence energy management with time and device-based preferences using an enhanced binary grey wolf optimization algorithm

机译：利用增强二进制灰羽智能优化算法，最佳停留能源管理与基于设备的偏好

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摘要

In residential energy management (REM), time of use (TOU) of appliances scheduling based on user-defined preferences is an essential task performed by the home energy management controller. This paper devised a robust REM technique capable of monitoring and controlling residential loads within a smart home. The method is based on an improved binary grey wolf accretive satisfaction algorithm (GWASA), which is developed based on four hypotheses that allow time-varying preferences to be quantifiable in terms of time and device-dependent features. Based on household appliances TOU, the absolute satisfaction derived from the preferences of appliance and power ratings, the GWASA can produce optimum energy consumption pattern that will give the customer maximum satisfaction at the predefined user budget. A cost per unit satisfaction index is also established to relate daily consumer expenses with the achieved satisfaction. Simulation results on three peak budgets from $1.5/day to $2.5/day with a step size of $0.5 are carried out to analyze the efficacy of GWASA. Accordingly, the result of each of the scenario is compared with the result obtained from three other different algorithms, namely, BPSO, BGA, BGWO. The simulation results reveal that the proposed demand side residential management based on GWASA offers the least cost per unit satisfaction and maximum percentage satisfaction in each scenario.
机译：在住宅能源管理（REM）中，基于用户定义的偏好的器具调度的使用时间（TOU）是由家用能管理控制器执行的基本任务。本文设计了一种强大的REM技术，能够监控和控制智能家庭内的住宅负载。该方法基于改进的二进制灰狼增持满意度算法（GWASA），其基于四个假设开发，其允许在时间和设备依赖性特征方面可以量化的时变偏好。基于家用电器Tou，Gwasa可以产生源于设备和电力额定值的偏好的绝对满足，可以产生最佳的能耗模式，这将使客户在预定义的用户预算中最大满意度。也建立了每单位满意度指数的成本，以使每日消费费用与实现的满意度。仿真结果从1.5美元/天的三个峰值预算到2.5美元/天/天的25美元，进行了0.5美元的步骤，以分析GWASA的功效。因此，将每个场景的结果与从其他三种不同算法获得的结果进行比较，即BPSO，BGA，BGWO。仿真结果表明，基于GWASA的拟议需求侧住宅管理提供了每种单位满意度的最低成本和每种情况的最大百分比满意度。

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