A hybrid sparse identification and convolutional neural network framework for renewable energy forecasting

A hybrid sparse identification and convolutional neural network framework for renewable energy forecasting

In the field of renewable energy, accurate long-term time series forecasting is crucial for optimizing the operation of power systems and reducing risks. Due to the intermittency of renewable energy sources, traditional data-driven deep learning methods face challenges in capturing long-term depende...

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Bibliographic Details
Main Authors:	Junchi He, Tian Tian, Yaqing Wu, Xiaolu Liu, Mengli Wei
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2024-12-01
Series:	Frontiers in Energy Research
Subjects:	time series forecasting long-term prediction sparse identification convolutional neural networks renewable energy
Online Access:	https://www.frontiersin.org/articles/10.3389/fenrg.2024.1461410/full
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