Optimizing Renewable Energy Forecasting Using Deep Learning Models for Enhanced Grid Management

EasyChair Preprint 15376

Abstract

Renewable energy forecasting is essential for efficient grid management, helping to balance supply and demand and integrate renewable sources into existing power systems. Deep learning models, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), offer promising solutions for accurate renewable energy prediction by capturing complex temporal and spatial dependencies in energy production data. This paper explores the use of deep learning in forecasting solar, wind, and hydropower generation, assessing model performance and evaluating their impact on grid stability and efficiency. Case studies highlight the effectiveness of deep learning in enhancing forecasting accuracy, contributing to improved grid management and reduced reliance on fossil fuels.

Keyphrases: Convolutional Neural Networks, Energy prediction, Forecasting, Grid Management, Recurrent Neural Networks, deep learning, renewable energy

@booklet{EasyChair:15376,
  author    = {Sophia Carlisle},
  title     = {Optimizing Renewable Energy Forecasting Using Deep Learning Models for Enhanced Grid Management},
  howpublished = {EasyChair Preprint 15376},
  year      = {EasyChair, 2024}}