Soft Ordering 1-D CNN: Wind Farm Capacity Factor Estimation Scripts

Wind energy engineering scripts for a Soft Ordering 1-D Convolutional Neural Network were developed by researchers from the University of Agder, NORCE, and TU Delft in 2024. The codebase provides the implementation for estimating capacity factors to identify age-related performance degradation in wind farms. This research was funded by the Research Council of Norway under grant no. 312486.

Use Cases

• Estimating the capacity factor of wind turbines using 1-D CNN layers
• Identifying age-related performance degradation trends in wind energy production
• Implementing Soft Ordering logic for deep learning models in engineering contexts

Strengths

• Developed by a multi-institutional consortium including University of Agder and TU Delft
• Funded by the Research Council of Norway (grant no. 312486)
• Implements the specific Soft Ordering 1-D CNN architecture

Limitations

• Primary content is Python code rather than a standalone raw dataset
• Input data requirements for the scripts are not detailed in the metadata

Provenance

Source

University of Agder, NORCE, and TU Delft

Freshness

Last updated March 2026