Open Access

ARTICLE

Short-Term Wind Power Prediction Based on WVMD and Spatio-Temporal Dual-Stream Network

Yingnan Zhao^*, Yuyuan Ruan, Zhen Peng

School of Computer and Science, Nanjing University of Information Science and Technology, Nanjing, 210044, China

* Corresponding Author: Yingnan Zhao. Email:

(This article belongs to the Special Issue: Machine Learning and Applications under Sustainable Development Goals (SDGs))

Computers, Materials & Continua 2024, 81(1), 549-566. https://doi.org/10.32604/cmc.2024.056240

Received 17 July 2024; Accepted 23 August 2024; Issue published 15 October 2024

Abstract

As the penetration ratio of wind power in active distribution networks continues to increase, the system exhibits some characteristics such as randomness and volatility. Fast and accurate short-term wind power prediction is essential for algorithms like scheduling and optimization control. Based on the spatio-temporal features of Numerical Weather Prediction (NWP) data, it proposes the WVMD_DSN (Whale Optimization Algorithm, Variational Mode Decomposition, Dual Stream Network) model. The model first applies Pearson correlation coefficient (PCC) to choose some NWP features with strong correlation to wind power to form the feature set. Then, it decomposes the feature set using Variational Mode Decomposition (VMD) to eliminate the non-stationarity and obtains Intrinsic Mode Functions (IMFs). Here Whale Optimization Algorithm (WOA) is applied to optimise the key parameters of VMD, namely the number of mode components K and penalty factor a. Finally, incorporating attention mechanism (AM), Squeeze-Excitation Network (SENet), and Bidirectional Gated Recurrent Unit (BiGRU), it constructs the dual-stream network (DSN) for short-term wind power prediction. Comparative experiments demonstrate that the WVMD_DSN model outperforms existing baseline algorithms and exhibits good generalization performance. The relevant code is available at (accessed on 20 August 2024).

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Keywords

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