"Adaptive Machine Learning Approaches for Forecasting Renewable Energy and Optimizing Smart Grids with Natural Elements"

EasyChair Preprint 14427

Abstract

The increasing reliance on renewable energy sources necessitates innovative approaches to efficiently forecast energy production and optimize smart grid operations. This research explores adaptive machine learning (ML) techniques for accurately predicting renewable energy generation and enhancing the management of smart grids. The study focuses on the integration of natural elements such as weather patterns, geographical data, and seasonal variations into ML models to improve forecasting accuracy. By employing adaptive algorithms, we aim to accommodate the dynamic nature of renewable energy sources and adapt to changing conditions in real-time. The proposed approach leverages deep learning architectures and reinforcement learning to optimize energy distribution, reduce grid inefficiencies, and enhance system resilience. Simulation results demonstrate the effectiveness of these adaptive ML models in handling fluctuations in energy supply and demand, leading to more reliable and sustainable energy management. This research contributes to the advancement of intelligent energy systems, providing insights into the potential of adaptive learning frameworks to support the transition towards greener energy solutions and smarter grid infrastructures.

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:14427,
  author    = {Kayode Sheriffdeen},
  title     = {"Adaptive Machine Learning Approaches for Forecasting Renewable Energy and Optimizing Smart Grids with Natural Elements"},
  howpublished = {EasyChair Preprint 14427},
  year      = {EasyChair, 2024}}