Optimizing Renewable Energy Management in Smart Grids Using Machine Learning

Abstract

Renewable energy management in smart grids is a challenging problem due to the uncertainty and variability of renewable energy sources. To improve the efficiency and reliability of renewable energy utilization, various optimization techniques have been proposed. In this paper propose an approach based on the Extreme Learning Machine (ELM) algorithm with Particle Swarm Optimization (PSO) for optimizing renewable energy management in smart grids. The ELM algorithm is used to model and predict renewable energy generation, while the PSO algorithm is used to optimize the parameters of the ELM algorithm. The proposed approach is evaluated on a dataset of solar energy production and compared with other optimization techniques. The results show that the ELM-PSO approach can improve the accuracy of renewable energy predictions and reduce energy costs in smart grids. The proposed approach can be used in various renewable energy systems, such as wind turbines, solar panels, and hydroelectric power plants, to improve the efficiency and reliability of renewable energy utilization.