"Integrating Supervised Machine Learning for Renewable Energy Forecasting with Nature-Inspired Optimization in Smart Energy Grids"

EasyChair Preprint 14424

Abstract

The increasing integration of renewable energy sources into smart energy grids presents significant challenges in maintaining grid stability and optimizing energy distribution. Accurate forecasting of renewable energy generation is crucial to address the inherent variability and unpredictability of these sources. This research explores the integration of supervised machine learning models with nature-inspired optimization algorithms to enhance the accuracy and reliability of renewable energy forecasting within smart energy grids. Supervised machine learning models, including Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Gradient Boosting Machines (GBMs), are employed to predict short-term and long-term energy outputs from renewable sources such as solar and wind. To improve these predictions, nature-inspired optimization techniques, including Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO), are used to fine-tune the machine learning models' hyperparameters and feature selection processes. The proposed approach aims to reduce forecasting errors, enhance grid management, and ensure efficient energy distribution. The integration of these advanced methodologies is validated through case studies on real-world data from smart grids, demonstrating significant improvements in forecasting accuracy and overall grid performance. This research contributes to the development of more resilient and efficient smart energy grids, capable of accommodating the growing presence of renewable energy sources.

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:14424,
  author    = {John Owen},
  title     = {"Integrating Supervised Machine Learning for Renewable Energy Forecasting with Nature-Inspired Optimization in Smart Energy Grids"},
  howpublished = {EasyChair Preprint 14424},
  year      = {EasyChair, 2024}}