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 language | English (US) |
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Pages (from-to) | 485-490 |
Number of pages | 6 |
Journal | ICT Express |
Volume | 10 |
Issue number | 3 |
DOIs | |
State | Published - Jun 2024 |
Externally published | Yes |
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