An Integrated Stacked Sparse Autoencoder and CNN-BLSTM Model for Ultra-Short-Term Wind Power Forecasting with Advanced Feature Learning

Liu, Jinjie; Kazemtabrizi, Behzad; Du, Hailiang; Matthews, Peter; Sun, Hongjian

Abstract

With the increasing integration of renewable energy sources into the power grid, accurate and reliable ultra-short-term forecasting of wind power is critical for optimizing grid stability and energy efficiency, especially for a highly dynamic and variable environment. This paper combines Stacked Sparse Autoencoders (SSAE) with a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BLSTM) architecture to address this challenge, which forms a novel deep learning framework, namely hybrid Stacked Sparse Autoencoder and Convolutional neural network-Bidirectional CNN-BLSTM with advanced Feature selection (SSACBF). The process starts with rigorous data preprocessing and key variable selection through a three-step approach based on expert and statistical methods. The framework employs a stacked sparse multi-layer CNN autoencoder to distil inputs into a robust feature set capturing complex temporal dependencies. These features are then processed by a CNN-BLSTM model, which leverages CNN layers for spatial-temporal nuances and BLSTM layers to simultaneously learn from past and future data. The approach significantly outperforms existing models in accuracy and efficiency, demonstrating potential for real-time applications in wind farm operational planning and energy management systems.

Citation

Liu, J., Kazemtabrizi, B., Du, H., Matthews, P., & Sun, H. (2024, November). An Integrated Stacked Sparse Autoencoder and CNN-BLSTM Model for Ultra-Short-Term Wind Power Forecasting with Advanced Feature Learning. Presented at 50th Annual Conference of the IEEE Industrial Electronics Society, Chicago, USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 50th Annual Conference of the IEEE Industrial Electronics Society
• Start Date: Nov 3, 2024
• End Date: Nov 6, 2024
• Acceptance Date: Jul 3, 2024
• Online Publication Date: Mar 10, 2025
• Publication Date: Mar 10, 2025
• Deposit Date: Oct 3, 2024
• Publicly Available Date: Mar 10, 2025
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Book Title: IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society
• DOI: https://doi.org/10.1109/IECON55916.2024.10905784
• Public URL: https://durham-repository.worktribe.com/output/2944025