Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation.

The supplementary materials provide the complete codebase for the Soft Ordering 1-D Convolutional Neural Network (1-D CNN) model for estimating the capacity factor of wind farms, as presented in \"Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation\" (PHME 2024). This research was funded by Analytics for Asset Integrity Management of Windfarms (AIMWind), under grant no. 312486, from the Research Council of Norway (RCN). AIMWind is a collaborative research project from the University of Agder, Norwegian Research Center (NORCE), and TU Delft, with DNV and Origo Solutions as advisory partners.

本补充材料提供了用于估算风电场容量因子的软排序一维卷积神经网络（1-D Convolutional Neural Network，1-D CNN）模型的完整代码库，相关成果已发表于《软排序一维卷积神经网络用于估算风电场容量因子以识别年龄相关性能衰减》（PHME 2024）。本研究由挪威研究理事会（Research Council of Norway，RCN）资助，资助项目为风电场资产完整性管理分析（Analytics for Asset Integrity Management of Windfarms，AIMWind），项目编号为312486。该项目由阿格德大学、挪威研究中心（Norwegian Research Center，NORCE）与代尔夫特理工大学（TU Delft）联合开展，DNV与Origo Solutions担任咨询合作伙伴。