"Machine Learning-Driven Renewable Energy Forecasting and Bio-Inspired Smart Grid Design for Sustainable Energy Management"

EasyChair Preprint 14426

Abstract

The increasing integration of renewable energy sources into the power grid presents significant challenges for reliable and efficient energy management. This research explores the potential of machine learning-driven renewable energy forecasting combined with bio-inspired smart grid design to enhance the sustainability and resilience of modern energy systems. Machine learning algorithms are utilized to predict energy generation from variable renewable sources such as solar and wind, enabling more accurate demand-supply balancing. Simultaneously, bio-inspired algorithms, modeled after natural systems' efficiency and adaptability, are employed to optimize grid operations, including load distribution and fault tolerance. This integrated approach aims to reduce the dependency on fossil fuels, minimize energy wastage, and improve grid stability, addressing both environmental and economic concerns. The study evaluates the performance of the proposed system through simulations and case studies, demonstrating its potential to revolutionize sustainable energy management. The findings suggest that the convergence of machine learning and bio-inspired designs in smart grids offers a promising pathway towards achieving a more sustainable and resilient energy infrastructure.

Keyphrases: Deep Learning in Energy Systems, Energy Forecasting, Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Renewable Energy Prediction, Smart Grid Optimization, climate change mitigation, hybrid machine learning, nature-inspired algorithms, sustainable energy systems

@booklet{EasyChair:14426,
  author    = {Alakitan Samad},
  title     = {"Machine Learning-Driven Renewable Energy Forecasting and Bio-Inspired Smart Grid Design for Sustainable Energy Management"},
  howpublished = {EasyChair Preprint 14426},
  year      = {EasyChair, 2024}}