The SOLETE dataset
Author: Daniel Vázquez Pombo, ORCID: https://orcid.org/0000-0001-5664-9421
The best way to contact me is through LinkedIn: https://www.linkedin.com/in/dvp/
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This item includes the SOLETE dataset which is disclosed in [1] to increase the transparency and replicability of [2, 3, 4].
SOLETE includes 15 months measurements with different resolutions (from second to hourly) from the 1st June 2018 to 1st September 2019 covering: Timestamp, air temperature, relative humidity, pressure, wind speed, wind direction, global horizontal irradiance, plane of array irradiance, and active power recorded from an 11 kW Gaia wind turbine and a 10 kW PV inverter.
The origin of the data is SYSLAB, part of DTU Wind and Energy Systems. If you want to learn more about the dataset check out [1].
You can use the SOLETE dataset with the codes available in GitHub: https://github.com/DVPombo/SOLETE/tree/main
The different scripts have various functions. One allows to import SOLETE and show some plots. Another is a platform where you can play with different Machine Learning models for time series forecasting. The application focuses on predicting PV power, but it can be easily edited by the user.
The publications related to this item are:
[1] Pombo, D. V., Gehrke, O., & Bindner, H. W. (2022). SOLETE, a 15-month long holistic dataset including: Meteorology, co-located wind and solar PV power from Denmark with various resolutions. Data in Brief, 42, 108046.
[2] Pombo, D. V., Bindner, H. W., Spataru, S. V., Sørensen, P. E., & Bacher, P. (2022). Increasing the accuracy of hourly multi-output solar power forecast with physics-informed machine learning. Sensors, 22(3), 749.
[3] Pombo, D. V., Bacher, P., Ziras, C., Bindner, H. W., Spataru, S. V., & Sørensen, P. E. (2022). Benchmarking physics-informed machine learning-based short term PV-power forecasting tools. Energy Reports, 8, 6512-6520.
[4] Pombo, D. V., Rincón, M. J., Bacher, P., Bindner, H. W., Spataru, S. V., & Sørensen, P. E. (2022). Assessing stacked physics-informed machine learning models for co-located wind–solar power forecasting. Sustainable Energy, Grids and Networks, 32, 100943.
To cite this item, I would appreciate if you use [1]. Alternatively, you can also use the following (but note that I won't get credit for it):
@misc{Pombo2022SOLETE, author = "Daniel Vazquez Pombo", title = "{The SOLETE dataset}", year = "2023", month = "Apr", url = "https://data.dtu.dk/articles/dataset/The_SOLETE_dataset/17040767", doi = "10.11583/DTU.17040767", note = {Retrieved from {DTU-Data}, url{https://data.dtu.dk/articles/dataset/The_SOLETE_dataset/17040767}, {DOI}: {10.11583/DTU.17040767}},}
