If you use the DSWE Datasets on this website in your research and publications, please kindly acknowledge the source by citing the book and the booksite. Citation of the book: Ding, Y. (2019) Data Science for Wind Energy, Chapman & Hall/CRC Press, Boca Raton, FL, and citation of the booksite: https://sites.google.com/view/yuding/book-dswe.
Book Datasets
The Book Datasets are hosted on Zenodo and each set is assigned a unique DOI. You are welcome to cite individual datasets using its DOI. The citation format is suggested on the Zenodo website in the section of “Cite as.”
3. Wind Spatio-Temporal Dataset1
4. Wind Spatio-Temporal Dataset2
5. Inland and Offshore Wind Farm Dataset1
6. Inland and Offshore Wind Farm Dataset2
9. Turbine Bending Moment Dataset
10. Simulated Bending Moment Dataset
Extension Datasets
After the book is published, we continue releasing additional datasets related to wind energy. These datasets are considered extension of the original book project and are now part of the DSWE Datasets. Please feel free to use them in your research. We ask you to acknowledge the source by citing the corresponding paper and this DSWE Datasets website.
Paired Turbine and Met Tower Datasets. [data][explanation of the dataset]
Energy Decomposition Datasets, used in Latiffianti, Ding, Sheng, Williams, Morshedizadeh, and Rodgers, 2022, “Analysis of leading edge protection application on wind turbine performance through energy and power decomposition approaches,” Wind Energy, Vol. 25(7), pp. 1203-1221. [datasets][code][reproducibility report][paper]
Temporal Overfitting Datasets, used in Prakash, Tuo, and Ding, 2023, “The temporal overfitting problem with applications in wind power curve modeling,” Technometrics, Vol. 65(1), pp. 70-82 [data] [code] [paper] [supplemental materials]
Turbine Terrain Datasets, used in Prakash, Lee, Liu, Liu, Mallick, and Ding, 2024, “A Bayesian hierarchical model to understand the effect of terrain on wind turbine power curves,” IEEE Transactions on Sustainable Energy, Vol.15(2), pp. 1127-1137. [data] [code] [reproducibility report] [paper]
Wind Forecasting Datasets, used in Kio, Xu, Gautam, and Ding, 2024, “Wavelet decomposition and neural networks: A potent combination for short term wind speed and power forecasting,” Frontiers in Energy Research, section of Wind Energy, Vol. 12, pp. 1277464 [data&code][reproducibility report] [paper]