"Scalable Supervised Learning Algorithms for Real-Time Renewable Energy Forecasting in Smart Grids"

EasyChair Preprint 14447

Abstract

The integration of renewable energy sources into modern smart grids presents significant challenges due to the variability and unpredictability of energy generation. Accurate real-time forecasting of renewable energy output is crucial for ensuring grid stability, optimizing energy distribution, and minimizing energy wastage. This research explores the development and application of scalable supervised learning algorithms tailored for real-time renewable energy forecasting in smart grids.

The study begins by reviewing the unique characteristics of renewable energy sources, such as solar and wind, and the implications of their variability on grid management. We analyze the limitations of existing forecasting methods and highlight the need for more advanced, scalable solutions that can process large volumes of data in real-time while adapting to the evolving nature of energy generation patterns.

Finally, this research discusses the practical implications of deploying these algorithms in smart grids, including potential challenges in data integration, model interpretability, and the need for continuous model updates. We conclude by outlining future research directions, emphasizing the importance of developing more adaptive algorithms that can incorporate emerging data sources and evolving energy market dynamics.

This study contributes to the ongoing efforts to enhance the reliability and efficiency of smart grids, supporting the broader goal of sustainable energy management and the transition to a low-carbon energy future.

Keyphrases: Deep Learning Models, Gradient Boosting, Random Forests, Recurrent Neural Networks (RNNs), Scalable Supervised Learning, Smart Grids, energy management, ensemble learning, real-time forecasting, renewable energy

@booklet{EasyChair:14447,
  author    = {Kayode Sheriffdeen},
  title     = {"Scalable Supervised Learning Algorithms for Real-Time Renewable Energy Forecasting in Smart Grids"},
  howpublished = {EasyChair Preprint 14447},
  year      = {EasyChair, 2024}}