Getting Started with DSWE

DSWE is a python package, written by Professor Yu Ding’s research team, to supplement the Data Science for Wind Energy (DSWE) book and other state-of-the-art methods used in wind energy applications.

DSWE is on PyPI.

DSWE Python HELP Document is available via this link.

Dependencies

DSWE requires:

  • Python (>=3.6)

  • NumPy (>=1.21.2)

  • Pandas (>=1.3.3)

  • Scikit-learn (>=1.0)

  • Scipy (>=1.7.0)

  • Statsmodels (>=0.13.0)

  • PyTorch (>=1.0.0)

  • Matplotlib (>=3.4.3)

All the required packages don’t need to be pre-installed. Installing DSWE would automatically install everything.

Install DSWE

$ pip install dswe

To get the latest code changes as they are merged, you can clone this repo and build from source manually.

$ git clone https://github.com/TAMU-AML/DSWE-Python/
$ pip install DSWE-Python/

Attention

  • AMK: The optimal bandwidth selection algorithm i.e., the direct plug-in (DPI) approach, is not implemented yet. You need to provide bandwidth corresponding to each column.

  • BayesTreePowerCurve: This module is built on top BartPy which is a python implementation of the Bayesian additive regressions trees (BART). To use the BayesTreePowerCurve model, you need to install the BartPy manually. The BartPy package has not been updated for a long time and simple pip install bartypy sometimes does not work. You have to explicitly clone the repo and build from source manually.

You can follow the following steps to install BartPy package.

$ git clone https://github.com/JakeColtman/bartpy
$ pip install bartpy/

References

  • Data Science for Wind Energy book.

  • AMK: Lee, Ding, Genton, and Xie, 2015, “Power curve estimation with multivariate environmental factors for inland and offshore wind farms,” Journal of the American Statistical Association, Vol. 110, pp. 56-67. [pdf]

  • TempGP: Prakash, Tuo, and Ding, 2022, “The temporal overfitting problem with applications in wind power curve modeling,” Technometrics, accepted. [preprint]

  • CovMatch: Shin, Ding, and Huang, 2018, “Covariate matching methods for testing and quantifying wind turbine upgrades,” Annals of Applied Statistics, Vol. 12(2), pp. 1271-1292. [accepted version]

  • FunGP: Prakash, Tuo, and Ding, 2022, “Gaussian process aided function comparison using noisy scattered data,” Technometrics, Vol. 64, pp. 92-102. [preprint]

  • ComparePCurve: Ding, Kumar, Prakash, Kio, Liu, Liu, and Li, 2021, “A case study of space-time performance comparison of wind turbines on a wind farm,” Renewable Energy, Vol. 171, pp. 735-746. [preprint]

  • DNNPowerCurve: The DNNPowerCurve function is partially based on Karami, Kehtarnavaz, and Rotea, 2021, “Probabilistic neural network to quantify uncertainty of wind power estimation,” arXiv:2106.04656. [preprint]. Our team refined the network architecture and tuned the training parameters.