Wind Farm Load Estimation

This repository provides the codebase to reproduce the analyses and results presented in our paper, currently under review, on wind farm load estimation. It includes data processing pipelines, analysis scripts, and visualization tools.

The dataset used in this project is available via Zenodo: https://doi.org/10.5281/zenodo.15380254

Dependencies

• Python version: 3.11.1
• This project uses a local copy of an OpenOA v.2.3 (https://github.com/NREL/OpenOA). Other dependencies are listed in the requirements.txt

Usage

The analysis workflow is organized in Jupyter notebooks:

1. 01_proc_raw_data.ipynb: Initial data processing and cleaning
2. 02_sampling.ipynb: Data sampling procedures
3. 03_gpr_training.ipynb: Gaussian Process Regression model training
4. 03_pce_training.ipynb: Polynomial Chaos Expansion model training
5. 04_case_study.ipynb: Application and evaluation of models in case study

Data Structure

data/

case_study/

• predictions.pkl: Stored model predictions for the case study analysis

models/

• gpr_models.pickle: Trained Gaussian Process Regression models
• pce_models.pickle: Trained Polynomial Chaos Expansion models

samples/

• sample_set.npy: Output of the 02_sampling.ipynb notebook.

scada/

• farmdata_*: Processed farmdata including all turbines, ready for application

simulation/

• sample_sim_setup/: A set of representative openfast files for one simulation case.
• casematrix.csv: Simulation case definitions. Transformed to .csv from sample_set.npy
• surrogate_data.csv: Processed 10min load variables by case number

turbines/

• IEA-3.4-130-RWT/: IEA reference wind turbine model files (https://github.com/IEAWindSystems/IEA-3.4-130-RWT)
• Adapted RWT model/: Above model with minor changes to better fit case study.

Citation

[Add citation for paper]

Contact

• Alexander Mönnig: alexander.moennig@alterric.com
• Ulrich Römer: u.roemer@tu-braunschweig.de