Supervised Learning Models for Predicting Renewable Energy Outputs and Integrating Nature-Based Algorithms in Smart Grid Optimization

EasyChair Preprint 14423

Abstract

The transition towards renewable energy sources is crucial for achieving sustainable energy systems, yet the inherent variability of these resources poses significant challenges in ensuring consistent and reliable energy supply. This research explores the application of supervised learning models to accurately predict renewable energy outputs, thereby enhancing the management and integration of renewable resources into the energy grid. Additionally, the study investigates the integration of nature-based algorithms, such as genetic algorithms and particle swarm optimization, into smart grid optimization processes. These algorithms are inspired by natural phenomena and have demonstrated efficiency in solving complex optimization problems. By combining predictive models with nature-based optimization techniques, this research aims to develop a robust framework that optimizes the performance and stability of smart grids. The proposed approach is expected to improve energy forecasting accuracy, enhance grid reliability, and support the large-scale deployment of renewable energy sources. The findings of this study will contribute to advancing smart grid technologies and promoting a more sustainable and resilient energy infrastructure.

Keyphrases: Energy Forecasting, Genetic Algorithms in Smart Grids, Hybrid optimization Techniques, LSTM for Energy Prediction, Machine Learning in Energy Systems, Nature-based algorithms, Renewable Energy Prediction, Smart Grid Optimization, Supervised Learning Models, renewable energy integration

@booklet{EasyChair:14423,
  author    = {Kayode Sheriffdeen},
  title     = {Supervised Learning Models for Predicting Renewable Energy Outputs and Integrating Nature-Based Algorithms in Smart Grid Optimization},
  howpublished = {EasyChair Preprint 14423},
  year      = {EasyChair, 2024}}