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Smart home energy management processes support through machine learning algorithms

dc.creatorKoltsaklis N., Panapakidis I., Christoforidis G., Knápek J.en
dc.date.accessioned2023-01-31T08:43:51Z
dc.date.available2023-01-31T08:43:51Z
dc.date.issued2022
dc.identifier10.1016/j.egyr.2022.01.033
dc.identifier.issn23524847
dc.identifier.urihttp://hdl.handle.net/11615/75041
dc.description.abstractSmart Home Energy Management Systems can manifest energy consumption reduction targets in the residential sector and can be viewed as an approach to transform the consumer into an active prosumer. The present paper presents a smart home energy management system that includes flexible appliances, electric vehicles, and energy storage units. Efficient forecasting algorithms support the robust operation of the smart home energy management system. Specifically, the smart home energy management system receives as inputs forecasts of demand, renewable energy sources including photovoltaics and Wind Turbine generations, and real-time prices. In order to minimize energy costs, a variety of algorithms is compared to provide highly accurate forecasts. © 2022 The Author(s)en
dc.language.isoenen
dc.sourceEnergy Reportsen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85123356876&doi=10.1016%2fj.egyr.2022.01.033&partnerID=40&md5=509ae34efa26e891579fde482ba4fbad
dc.subjectAutomationen
dc.subjectCostsen
dc.subjectEnergy managementen
dc.subjectEnergy management systemsen
dc.subjectEnergy utilizationen
dc.subjectLearning algorithmsen
dc.subjectMachine learningen
dc.subjectRenewable energy resourcesen
dc.subjectDemand responseen
dc.subjectEnergy-consumptionen
dc.subjectHome energy managementsen
dc.subjectMachine learning algorithmsen
dc.subjectManagement processen
dc.subjectOptimisationsen
dc.subjectProcess supporten
dc.subjectProsumeren
dc.subjectSmart home energy management systemsen
dc.subjectSmart homesen
dc.subjectForecastingen
dc.subjectElsevier Ltden
dc.titleSmart home energy management processes support through machine learning algorithmsen
dc.typejournalArticleen


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