Optimization of Home Energy Management System Through Application of Tabu Search

Shafiq, Sundas; Fatima, Iqra; Abid, Samia; Asif, Sikandar; Ansar, Sajeeha; Abideen, Zain Ul; Javaid, Nadeem

doi:10.1007/978-3-319-69835-9_4

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Several meta-heuristic algorithms for RASP are studied in the literature. Genetic algorithm (GA) [63,71], particle swarm optimization (PSO) [99], wind-driven optimization (WDO) [100], ant colony optimization (ACO) [63] are some of the most prominent meta-heuristic algorithms. The existing research works in meta-heuristic algorithms involve HEMS-based appliance scheduling methods.…”

Section: Meta-heuristics Algorithmsmentioning

confidence: 99%

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An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem

et al. 2020

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Smart grid (SG) is a next-generation grid which is responsible for changing the lifestyle of modern society. It avoids the shortcomings of traditional grids by incorporating new technologies in the existing grids. In this paper, we have presented SG in detail with its features, advantages, and architecture. The demand side management techniques used in smart grid are also presented. With the wide usage of domestic appliances in homes, the residential users need to optimize the appliance scheduling strategies. These strategies require the consumer’s flexibility and awareness. Optimization of the power demand for home appliances is a challenge faced by both utility and consumers, particularly during peak hours when the consumption of electricity is on the higher side. Therefore, utility companies have introduced various time-varying incentives and dynamic pricing schemes that provides different rates of electricity at different times depending on consumption. The residential appliance scheduling problem (RASP) is the problem of scheduling appliances at appropriate periods considering the pricing schemes. The objectives of RASP are to minimize electricity cost (EC) of users, minimize the peak-to-average ratio (PAR), and improve the user satisfaction (US) level by minimizing waiting times for the appliances. Various methods have been studied for energy management in residential sectors which encourage the users to schedule their appliances efficiently. This paper aims to give an overview of optimization techniques for residential appliance scheduling. The reviewed studies are classified into classical techniques, heuristic approaches, and meta-heuristic algorithms. Based on this overview, the future research directions are proposed.

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Section: Meta-heuristics Algorithmsmentioning

confidence: 99%

“…• Many meta-heuristic algorithms such as ACO [63], GA [71], PSO [99] are inspired by natural phenomena. These algorithms explore the alternative meaning of optimizing RASP objectives and scheduling the power profile of home appliances at any hour of the day.…”

Section: Meta-heuristics Algorithmsmentioning

confidence: 99%