IoT enabled intelligent energy management system employing advanced forecasting algorithms and load optimization strategies to enhance renewable energy generation

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dc.contributor.authorRao, Challa Krishna
dc.contributor.authorSahoo, Sarat Kumar
dc.contributor.authorYanine, Fernando
dc.date.accessioned2024-12-03T18:09:56Z
dc.date.available2024-12-03T18:09:56Z
dc.date.issued2024-08-08
dc.description.abstractEffectively utilizing renewable energy sources while avoiding power consumption restrictions is the problem of demand-side energy management. The goal is to develop an intelligent system that can precisely estimate energy availability and plan ahead for the next day in order to overcome this obstacle. The Intelligent Smart Energy Management System (ISEMS) described in this work is designed to control energy usage in a smart grid environment where a significant quantity of renewable energy is being introduced. The proposed system evaluates various predictive models to achieve accurate energy forecasting with hourly and day-ahead planning. When compared to other predictive models, the Support Vector Machine (SVM) regression model based on Particle Swarm Optimization (PSO) seems to have better performance accuracy. Then, using the anticipated requirements, the experimental setup for ISEMS is shown, and its performance is evaluated in various configurations while considering features that are prioritized and associated with user comfort. Furthermore, Internet of Things (IoT) integration is put into practice for monitoring at the user end.
dc.identifier.citationUnconventional Resources, Vol. 4 N°100101 (2024) p. 1-14.
dc.identifier.doihttps://doi.org/10.1016/j.uncres.2024.100101
dc.identifier.issn2666-5190
dc.identifier.orcidhttps://orcid.org/0000-0003-1086-0840
dc.identifier.urihttp://hdl.handle.net/20.500.12254/3963
dc.language.isoen
dc.publisherElsevier
dc.rightsAtribución-NoComercial-CompartirIgual 3.0 Chile (CC BY-NC-SA 3.0 CL)
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/cl/
dc.subjectRenewable generation
dc.subjectEnergy consumption
dc.subjectLoad modeling
dc.subjectSmart grids
dc.subjectDemand-side energy management
dc.subjectMachine learning
dc.subjectIoT
dc.subjectEnergy management systems
dc.subjectForecast
dc.titleIoT enabled intelligent energy management system employing advanced forecasting algorithms and load optimization strategies to enhance renewable energy generation
dc.typeArticle
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