Enhancing wind speed forecasting accuracy using a GWO-nested CEEMDAN-CNN-BiLSTM model

Quoc Bao Phan, Tuy Tan Nguyen

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

This study introduces an advanced artificial model, grey wolf optimization (GWO)-nested complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-convolutional neural network (CNN)-bidirectional long short-term memory (BiLSTM), for wind speed forecasting. Initially, CEEMDAN with two nested layers decomposes the time series into intrinsic mode functions (IMFs) to enhance forecasting capabilities. Subsequently, CNN extracts features from IMFs, and BiLSTM captures temporal dependencies for precise predictions. GWO further enhances the accurac by selecting optimal hyperparameters based on decomposition results. Test results on diverse wind speed datasets demonstrate the model's superiority, with a mean absolute percentage error (MAPE) of approximately 3%.

Original languageEnglish (US)
Pages (from-to)485-490
Number of pages6
JournalICT Express
Volume10
Issue number3
DOIs
StatePublished - Jun 2024
Externally publishedYes

Keywords

  • Bidirectional long short-term memory
  • Convolutional neural network
  • Decomposition
  • Optimization
  • Wind speed forecasting

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

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