Skip to main content

ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Smart Grids
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1461410

A Hybrid Sparse Identification and Convolutional Neural Network Framework for Renewable Energy Forecasting

Provisionally accepted
Junchi He Junchi He 1Tian Tian Tian Tian 2Yaqing Wu Yaqing Wu 3Xiaolu Liu Xiaolu Liu 4*Mengli Wei Mengli Wei 3*
  • 1 China University of Mining and Technology, Xuzhou, Jiangsu Province, China
  • 2 State Grid Lianyungang Power Supply Company, Lianyugang, China
  • 3 Southeast University, Nanjing, China
  • 4 Nanjing Institute of Technology (NJIT), Nanjing, Jiangsu, China

The final, formatted version of the article will be published soon.

    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 dependencies. This paper proposes a hybrid model that combines Sparse Identification (SI) with Convolutional Neural Networks (CNN) to enhance the interpretability and generalization of predictions. The SI method is utilized to extract trends, seasonality, and periodicity, while the deep neural network captures complex relationships. Experimental results demonstrate that the model exhibits high accuracy and practicality in forecasting new energy scenario data, contributing to the advancement of time series prediction methodologies.

    Keywords: time series forecasting, Long-term prediction, Sparse identification, Convolutional Neural Networks, Renewable Energy

    Received: 08 Jul 2024; Accepted: 15 Nov 2024.

    Copyright: © 2024 He, Tian, Wu, Liu and Wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Xiaolu Liu, Nanjing Institute of Technology (NJIT), Nanjing, 211167, Jiangsu, China
    Mengli Wei, Southeast University, Nanjing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.