Spatiotemporal Wind Energy Forecasting: A Comprehensive Survey and a Deep Equilibrium-Based Case Study With StemGNN

Spatiotemporal Wind Energy Forecasting: A Comprehensive Survey and a Deep Equilibrium-Based Case Study With StemGNN

Accurate spatiotemporal wind energy forecasting is essential for ensuring grid stability and maximizing the efficiency of renewable energy systems. This paper addresses the challenge of modeling the complex spatial and temporal dependencies inherent in wind power generation by presenting a comprehen...

Full description

Saved in:
Bibliographic Details
Main Authors: Luiza Scapinello Aquino, Laio Oriel Seman, Viviana Cocco Mariani, Leandro Dos Santos Coelho, Stefano Frizzo Stefenon, Gabriel Villarrubia Gonzalez
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Wind energy
graph neural network
deep equilibrium
spatiotemporal forecasting
Online Access:https://ieeexplore.ieee.org/document/11095688/
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Accurate spatiotemporal wind energy forecasting is essential for ensuring grid stability and maximizing the efficiency of renewable energy systems. This paper addresses the challenge of modeling the complex spatial and temporal dependencies inherent in wind power generation by presenting a comprehensive survey of existing spatiotemporal forecasting methods and introducing an innovative deep learning approach. The proposed model integrates a Graph Neural Network (GNN) to represent wind turbines as nodes within a graph, capturing spatial relationships, while a Deep Equilibrium Model (DEQ) enables equilibrium-based inference to handle highly nonlinear wind patterns. A Sequence-to-Sequence (Seq2Seq) architecture further manages temporal dependencies. The method was validated using a real-world dataset of wind power generation, outperforming baseline models across multiple forecast horizons and maintaining stable accuracy across short- and mid-term predictions. Results demonstrate that the proposed GNN with DEQ effectively models both spatial and temporal dynamics for Seq2Seq data, improving prediction accuracy while maintaining computational efficiency. This study highlights the potential of equilibrium-based spatiotemporal graph models for wind energy forecasting and provides a robust tool for better integration of wind power into modern power grids.
ISSN:2169-3536

Similar Items