Publication:
Fuzzy Based Particle Swarm Optimization for Modeling Home Appliances towards Energy Saving and Cost Reduction under Demand Response Consideration

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dc.citedby21
dc.contributor.authorParvin K.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorAl-Shetwi A.Q.en_US
dc.contributor.authorKer P.J.en_US
dc.contributor.authorRoslan M.F.en_US
dc.contributor.authorMahlia T.M.I.en_US
dc.contributor.authorid57203309143en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid57004922700en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid57220188085en_US
dc.contributor.authorid56997615100en_US
dc.date.accessioned2023-05-29T08:12:08Z
dc.date.available2023-05-29T08:12:08Z
dc.date.issued2020
dc.descriptionAir conditioning; Constrained optimization; Controllers; Cost benefit analysis; Cost reduction; Electric power utilization; Energy efficiency; Energy management systems; Energy utilization; Fuzzy logic; Gas emissions; Greenhouse gases; Heating; HVAC; Lighting; Membership functions; Particle swarm optimization (PSO); Solar buildings; Traffic signals; Water heaters; Electric water heaters; Energy consumption and cost; Fuzzy logic controllers; Heating ventilation and air conditioning; Optimal performance; Optimized controllers; Sustainable energy; Sustainable environment; Domestic appliancesen_US
dc.description.abstractRecently, homes consume around 40% of world power and produce 21% of the total greenhouse gas emissions. Thus, the proper management of energy in the domestic sector is a vital element for creating a sustainable environment and cost reduction. In this study, an intelligent home energy management system (HEMS) is developed to control domestic appliances load. The motivation of this work is reduced the electricity cost and power consumption from all the appliances by maintaining the customer's high comfort level using an efficient optimized controller. The domestic household appliances such as heating ventilation and air conditioning (HVAC), electric water heater (EWH) and lighting were modelled and analysed using Simulink/Matlab. The developed models analysed the appliances' energy consumption and cost sceneries during peak, off-peak and both peak and off-peak hours. Fuzzy logic controller (FLC) was developed for the HEMS to perform energy utilization estimation and cost analysis during these periods taking the Malaysian tariff for domestic use into consideration. To improve the FLC outcomes and the membership function constraint, particle swarm optimization (PSO) is developed to ensure an optimal cost and power consumption. The results showed that the developed FLC controller minimized the cost and energy consumption for peak period by 19.72% and 20.34%, 26.71% and 26.67%, 37.5% and 33.33% for HVAC, EWH, and dimmable lamps, respectively. To validate the optimal performance, the obtained results shows that the FLC-PSO can control the home appliances more significantly compared to FLC only. In this regard, the FLC-PSO based optimum scheduled controller for the HEMS minimized power and cost by 36.17%-36.54%, 54.54%-55.76%, and 62.5%-58% per day for HVAC, EWH, and light, respectively. In sum, the PSO shows good performance to reduce the cost and power consumption toward efficient HEMS. Thus, the developed fuzzy-based heuristic optimized controller of HEMS is beneficial towards sustainable energy utilization. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9268107
dc.identifier.doi10.1109/ACCESS.2020.3039965
dc.identifier.epage210799
dc.identifier.scopus2-s2.0-85097348310
dc.identifier.spage210784
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097348310&doi=10.1109%2fACCESS.2020.3039965&partnerID=40&md5=b9854b87d1340f1e08a6a9577b5860a8
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25641
dc.identifier.volume8
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.titleFuzzy Based Particle Swarm Optimization for Modeling Home Appliances towards Energy Saving and Cost Reduction under Demand Response Considerationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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