Appliance scheduling in a smart home using a multiobjective evolutionary algorithm

Garroussi, Zineb; Ellaia, Rachid; Talbi, El‐Ghazali

doi:10.1109/irsec.2016.7983978

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…GA was compared with ACO [107] and also with Particle Swarm Optimization (PSO) [108,109] for maximizing user comfort. Multi-objective optimization problems were solved using a non-dominated sorting GA [110,111] and PSO [112]. The works in [113,114] used Artificial Bee Colony for appliance scheduling for energy management considering renewables as well.…”

Section: Ec Based Dsmmentioning

confidence: 99%

A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment

et al. 2021

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The ever increasing demand for electricity and the rapid increase in the number of automatic electrical appliances have posed a critical energy management challenge for both utilities and consumers. Substantial work has been reported on the Home Energy Management System (HEMS) but to the best of our knowledge, there is no single review highlighting all recent and past developments on Demand Side Management (DSM) and HEMS altogether. The purpose of each study is to raise user comfort, load scheduling, energy minimization, or economic dispatch problem. Researchers have proposed different soft computing and optimization techniques to address the challenge, but still it seems to be a pressing issue. This paper presents a comprehensive review of research on DSM strategies to identify the challenging perspectives for future study. We have described DSM strategies, their deployment and communication technologies. The application of soft computing techniques such as Fuzzy Logic (FL), Artificial Neural Network (ANN), and Evolutionary Computation (EC) is discussed to deal with energy consumption minimization and scheduling problems. Different optimization-based DSM approaches are also reviewed. We have also reviewed the practical aspects of DSM implementation for smart energy management.

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Section: Ec Based Dsmmentioning

confidence: 99%

A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment

et al. 2021

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“…The analysis of instant power graphs from Figures 9 and 10 demonstrates how PDe1 is distributed over a whole day and by circuit in Figure 11. Several studies [23,24,[42][43][44][45] identify the control of electric circuits as a solution to increase energy coupling, but in all cases using complex and expensive smart appliances, as well as building monitoring and control facilities, which is not the aim of this study. In Figure 11, C1 (DHW), C6 (COOK), C7 (WASH), and C8 (FRIDGE) provide the highest instant power values, but C8 (FRIDGE) cannot be rescheduled according to its own characteristics.…”

Section: Primary Energy Consumption In Scenariomentioning

confidence: 99%

“…The rest of the circuits (POe1) have a very regular and low profile. Therefore, these circuits can only be improved by the use of more complex, expensive devices [23,25,42]. The power of PSe1 shows a peak value of circa 1000 w, while the POe1 has a constant value of 200 W most of the time, in which both can be supplied by PP through the PV array at peak production.…”

Section: Primary Energy Consumption In Scenariomentioning

confidence: 99%

Matching Energy Consumption and Photovoltaic Production in a Retrofitted Dwelling in Subtropical Climate without a Backup System

et al. 2020

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The construction sector is a great contributor to global warming both in new and existing buildings. Minimum energy buildings (MEBs) demand as little energy as possible, with an optimized architectural design, which includes passive solutions. In addition, these buildings consume as low energy as possible introducing efficient facilities. Finally, they produce renewable energy on-site to become zero energy buildings (ZEBs) or even plus zero energy buildings (+ZEB). In this paper, a deep analysis of the energy use and renewable energy production of a social dwelling was carried out based on data measurements. Unfortunately, in residential buildings, most renewable energy production occurs at a different time than energy demand. Furthermore, energy storage batteries for these facilities are expensive and require significant maintenance. The present research proposes a strategy, which involves rescheduling energy demand by changing the habits of the occupants in terms of domestic hot water (DHW) consumption, cooking, and washing. Rescheduling these three electric circuits increases the usability of the renewable energy produced on-site, reducing the misused energy from 52.84% to 25.14%, as well as decreasing electricity costs by 58.46%.

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“…This module is responsible for cost reduction by constantly communicating with utility server regarding load patterns. Zineb et al in Reference [24] propose a HEMS for the scheduling of appliances in a home with an objective to decrease the total bill and waiting time of the appliances used in the home. The authors formulate the multi-objective problem and solve it using EA.…”

Section: Related Workmentioning

confidence: 99%

An Innovative Home Energy Management Model with Coordination among Appliances using Game Theory

et al. 2019

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The feature of bidirectional communication in a smart grid involves the interaction between consumer and utility for optimizing the energy consumption of the users. For optimal management of the energy at the end user, several demand side management techniques are implemented. This work proposes a home energy management system, where consumption of household appliances is optimized using a hybrid technique. This technique is developed from cuckoo search algorithm and earthworm algorithm. However, there is a problem in such home energy management systems, that is, an uncertain behavior of the user that can lead to force start or stop of an appliance, deteriorating the purpose of scheduling of appliances. In order to solve this issue, coordination among appliances for rescheduling is incorporated in home energy management system using game theory. The appliances of the home are categorized in three different groups and their electricity cost is computed through the real-time pricing signals. Optimization schemes are implemented and their performance is scrutinized with and without coordination among the appliances. Simulation outcomes display that our proposed technique has minimized the total electricity cost by 50.6% as compared to unscheduled cost. Moreover, coordination among appliances has helped in increasing the user comfort by reducing the waiting time of appliances. The Shapley value has outperformed the Nash equilibrium and zero sum by achieving the maximum reduction in waiting time of appliances.

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Appliance scheduling in a smart home using a multiobjective evolutionary algorithm

Cited by 7 publications

References 11 publications

A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment

A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment

Matching Energy Consumption and Photovoltaic Production in a Retrofitted Dwelling in Subtropical Climate without a Backup System

An Innovative Home Energy Management Model with Coordination among Appliances using Game Theory

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