The SOLETE dataset

Author: Daniel Vázquez Pombo, ORCID: https://orcid.org/0000-0001-5664-9421The best way to contact me is through LinkedIn: https://www.linkedin.com/in/dvp/-------------------------------------------------------------------------------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].<br>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.<br>The publications related to this item are:<br>[1] Pombo, D. V., Gehrke, O., &amp; 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., &amp; 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., &amp; 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., &amp; 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.<br>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}},}<br>

搜集汇总

背景与挑战

背景概述

SOLETE数据集是一个包含15个月气象和可再生能源发电数据的综合数据集，时间跨度为2018年6月至2019年9月，涵盖多种气象参数和发电功率记录，支持机器学习应用。