Research on anomaly detection in energy engineering bidding based on spatiotemporal graph neural network

Key Points

• The research aims to improve anomaly detection techniques in energy engineering bidding processes using advanced neural network models.
• Utilized spatiotemporal graph neural networks for analysis.
• Focused on energy engineering bidding data to identify irregular patterns.
• Employed machine learning approaches to enhance detection accuracy.
• Identified significant anomalies in bidding processes.
• Proposed methods show improved detection rates compared to traditional techniques.
• Demonstrated potential for optimizing decision-making in energy engineering.

Abstract