Rao, Challa KrishnaSahoo, Sarat KumarYanine, Fernando2024-12-032024-12-032024-08-29Electrical Engineering, Vol. 106, N° 5 (2024) p. 1-230948-7921http://hdl.handle.net/20.500.12254/3964Effectively 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 added. The proposed system evaluates various prediction models to achieve accurate energy forecasting with hourly and day-ahead planning. When compared to other prediction models, the Support Vector Machine (SVM) regression model based on Particle Swarm Optimization (PSO) seems to have better performance accuracy. Then, using the anticipated data, the experimental setup for ISEMS is shown, and its performance is evaluated in various configurations while considering features that are prioritized and user comfort. Furthermore, Internet of Things (IoT) integration is put into practice for monitoring at the user end.enAtribución-NoComercial-CompartirIgual 3.0 Chile (CC BY-NC-SA 3.0 CL)Renewable generationEnergy consumptionLoad modelingSmart gridsDemand-side energy managementMachine learningEnergy management systemsForecastArticlehttps://orcid.org/0000-0003-1086-0840https://doi.org/10.1007/s00202-024-02674-41432-0487