Wind Speed Forecasting Using an Ensemble Deep Random Vector Functional Link Neural Network Based on Parsimonious Channel Mixing

Ruke Cheng*, Ruobin Gao, Minghui Hu, Ponnuthurai Nagaratnam Suganthan, Kum Fai Yuen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The electricity generation through wind energy is rapidly expanding, primarily due to its priorities of lower carbon emissions and sustainability. Precise wind speed forecasting is essential for renewable energy conversions as it mitigates the randomness of wind power, therefore aiding in more effective control and strategic planning for power system dispatch. However, the inherent fluctuation of wind speed challenges accurate and consistent time series forecasting. In this paper, we develop a novel parsimonious channel mixing ensemble deep random vector functional link (pcm-edRVFL) network to anticipate future wind speeds. The ensemble deep random vector functional link network (edRVFL) utilizes deep feature extraction and ensemble learning to improve forecasting performance. We refined the standard edRVFL model by incorporating a parsimonious channel mixing selection approach for input data, focusing on crucial historical observations, and strengthening the representation of each explanatory variable. We conduct extensive evaluations on four wind speed datasets using the proposed model, and the comparative experiment results demonstrate its superiority over other baseline models. Our proposed pcm-edRVFL network provides a practical approach for precise and efficient wind speed forecasting, proving to be an instrumental resource in wind energy design and operation systems.

Original languageEnglish
Title of host publication2024 International Joint Conference on Neural Networks, IJCNN 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350359312
DOIs
Publication statusPublished - 2024
Externally publishedYes
Event2024 International Joint Conference on Neural Networks, IJCNN 2024 - Yokohama, Japan
Duration: Jun 30 2024Jul 5 2024

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2024 International Joint Conference on Neural Networks, IJCNN 2024
Country/TerritoryJapan
CityYokohama
Period6/30/247/5/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

ASJC Scopus Subject Areas

  • Software
  • Artificial Intelligence

Keywords

  • Deep Learning
  • Random Vector Functional Link
  • Time Series Forecasting
  • Wind Speed

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