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Optimal Scheduling of Home Appliances in Home Energy Management Systems Using Grey Wolf Optimisation (Gwo) Algorithm

机译:基于灰狼优化(Gwo)算法的家庭能源管理系统中家用电器的优化调度

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In smart homes, under price-based or incentivebased demand response programs, home energy management system (HEMS) aims to determine optimal schedule of appliances in order to minimise electricity bill of the home. This scheduling problem is commonly formulated as a constrained optimisation problem with integer decision variables. Metaheuristics are the most popular algorithms for solving engineering optimisation problems. Grey wolf optimisation (GWO) is a swarm-based metaheuristic optimisation algorithm, inspired from the performance of wolves and has shown promising performance in solving some engineering optimisation problems. In this paper, GWO is used for solving the problem of optimal scheduling of appliances in HEM systems. The problem is solved for two different homes with different set of appliances. For each home, the problem is solved for two cases with different DR programs. The performance of GWO is compared with the well-established particle swarm optimisation (PSO) algorithm. The results indicate the outperformance of the proposed GWO with respect to PSO.
机译:在智能家居中,根据基于价格或基于激励的需求响应计划,家庭能源管理系统(HEMS)的目的是确定最佳的家用电器时间表,以最大程度地减少家庭的电费。通常将该调度问题表述为具有整数决策变量的约束优化问题。元启发式算法是解决工程优化问题的最流行算法。灰狼优化(GWO)是一种基于群体的元启发式优化算法,其灵感来自于狼的性能,并且在解决某些工程优化问题方面显示出令人鼓舞的性能。在本文中,GWO用于解决HEM系统中设备的最佳调度问题。对于具有不同套具的两个不同家庭,该问题得以解决。对于每个家庭,使用不同的DR程序解决了两种情况下的问题。将GWO的性能与完善的粒子群优化(PSO)算法进行比较。结果表明,相对于PSO,建议的GWO的性能优于其他。

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