Technical University of Denmark
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The SOLETE dataset

posted on 2022-02-03, 07:34 authored by Daniel PomboDaniel Pombo

Author: Daniel Vázquez Pombo (, ORCID:


This item includes the SOLETE dataset which is disclosed to increase the transparency and replicability of [1] and [2], which are at different stages of the review process.

SOLETE includes 15 months of 5 minute and hourly measurements 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 Elektro. If you want to learn more about the dataset, you should check out [3].

You can use the SOLETE dataset with the codes available here:

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] D. V. Pombo, H. W. Bindner, S. V. Spataru, P. E. Sørensen, P. Bacher, Increasing the Accuracy of Hourly Multi-Output Solar Power Forecast with Physics-Informed Machine Learning, Sensors 22 (3) (2022) 749.

[2] D.V. Pombo, P. Bacher, C. Ziras, H.W. Bindner, S.V. Spataru, P. Sørensen, Benchmarking Physics-Informed Machine Learning-based Short Term PV-Power Forecasting Tools, Under Review.

[3] D.V. Pombo, O.G. Gehrke, H.W. Bindner, (2022). SOLETE, a 15-month long holistic dataset including: Meteorology, co-located wind and solar PV power from. Data in Brief, 108046.


To cite this item:



author = "Daniel Vazquez Pombo",

title = "{The SOLETE dataset}", 

year = "2022",

month = "Feb",

url = "",

doi = "10.11583/DTU.17040767",

note = {Retrieved from {DTU-Data}, \url{}, {DOI}: {10.11583/DTU.17040767}},