The SOLETE platform
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/
Note that the files in this repository might not be the latest. Go to the git for the most up-to-date version. https://github.com/DVPombo/SOLETE
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This item is Machine Learning platform aimed at machine learning forecasting of time series focused on PV power. The code is disclosed to facilitate the usability of [1], to increase the transparency and replicability of [2, 3, 4].
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.
For more information, dependencies, etc. Refer to the git: https://github.com/DVPombo/SOLETE
The platform should be useful as a learning tool in the machine learning field.
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, you should check out [1].
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.