Hybrid Wind Power Forecasting for Turbine Clusters: Integrating Spatiotemporal WGANs with Extreme Missing-Data Resilience

Hongsheng Su, Yuwei Du (), Yulong Che, Dan Li and Wenyao Su
Additional contact information
Hongsheng Su: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Yuwei Du: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Yulong Che: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Dan Li: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Wenyao Su: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Sustainability, 2025, vol. 17, issue 20, 1-18

Abstract: The global pursuit of sustainable development amplifies renewable energy’s strategic importance, positioning wind power as a vital modern grid component. Accurate wind forecasting is essential to counter inherent volatility, enabling robust grid operations, security protocols, and optimization strategies. Such predictive precision directly governs wind energy systems’ stability and sustainability. This research introduces a novel spatio-temporal hybrid model integrating convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and graph convolutional networks (GCN) to extract temporal patterns and meteorological dynamics (wind speed, direction, temperature) across 134 wind turbines. Building upon conventional methods, our architecture captures turbine spatio-temporal correlations while assimilating multivariate meteorological characteristics. Addressing data integrity compromises from equipment failures and extreme weather-which undermine data-driven models-we implement Wasserstein GAN (WGAN) for generative missing-value interpolation. Validation across severe data loss scenarios (30–90% missing values) demonstrates the model’s enhanced predictive capacity. Rigorous benchmarking confirms significant accuracy improvements and reduced forecasting errors.

Keywords: wind power forecasting; spatiotemporal correlation; generative adversarial networks; missing-data resilience; turbine cluster prediction; deep hybrid learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/20/9200/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/20/9200/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:20:p:9200-:d:1773332

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().