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].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}},}作者：丹尼尔·巴斯克斯·庞博（Daniel Vázquez Pombo），ORCID: https://orcid.org/0000-0001-5664-9421。最佳联系方式为领英：https://www.linkedin.com/in/dvp/ ------------------------------------------------------------------------------- 本数据集包含SOLETE数据集，该数据集已于文献[1]中公开，旨在提升文献[2, 3, 4]相关研究的透明度与可复现性。 SOLETE数据集涵盖2018年6月1日至2019年9月1日期间共15个月的多分辨率测量数据（分辨率范围从秒级至小时级），包含以下内容：时间戳、空气温度、相对湿度、气压、风速、风向、总水平辐照度、阵列平面辐照度，以及从11 kW Gaia风力涡轮机和10 kW光伏逆变器（PV inverter）采集的有功功率。 本数据来源于SYSLAB实验室，该实验室隶属于丹麦技术大学风能与能源系统系（DTU Wind and Energy Systems）。如需了解该数据集的更多详情，请查阅文献[1]。 用户可通过GitHub上的代码使用SOLETE数据集：https://github.com/DVPombo/SOLETE/tree/main。这些脚本具备多种功能：其一可导入SOLETE数据集并生成相关可视化图表；其二是一个平台，支持针对时间序列预测（time series forecasting）任务调试不同机器学习模型，该应用聚焦于光伏功率预测，但用户可轻松对其进行自定义修改。 与本数据集相关的出版物如下： [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. 若需引用本数据集，推荐使用文献[1]。您也可选用以下引用格式（请注意，此格式下我将无法获得学术贡献标注）： @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}},}