Find research datasets worth reusing

opencc-by-4.0May 2023View details →

TEAMx-PC22 (TEAMx pre-campaign 2022) - ACINN Doppler wind lidar data sets (SL88, SLXR142)

ABSTRACT The data sets found here were collected with <a href="http://acinn.uibk.ac.at/">ACINN</a>'s Doppler wind lidars SL88 and SLXR142 in Innsbruck, Austria, in summer 2022 in the framework of the TEAMx pre-campaign 2022 (TEAMx-PC22). The aim of TEAMx-PC22 was to test new instruments, new instrument configurations and new measurement sites to support the planning of the main TEAMx observational campaign (TOC) in 2024/2025. More details about TEAMx can be found at <a href="http://www.teamx-programme.org">http://www.teamx-programme.org</a> as well as in Serafin et al. (2020) and in Rotach et al. (2022). DATA SET DESCRIPTION 1. Spatial coverage and locations Measurements with the SL88 and SLXR142 lidar were collected during TEAMx-PC22 in Innsbruck, Austria, at the Campus Innrain of the University of Innsbruck. More specifically, the SLXR142 lidar was located on the rooftop of one of the university buildings (Bruno-Sander-Haus) at Innrain 52f. The SL88 lidar was located in the forecourt of the Campus Innrain, the so-called GEIWI-Forum, next to the Bruno-Sander-Haus. The exact lidar locations are: <ul> <li>SL88: 47.264083°N / 11.384986°E / 575 m MSL</li> <li>SLXR142: 47.26431°N / 11.38529°E / 613 m MSL</li> </ul> 2. Temporal coverage The TEAMx-PC22 lasted from mid-May 2022 to early October 2022. However, the SL88 data set contains a shorter period from 11 August to 02 October 2022 (1 Hz data, vertical stares). The SLXR142 data set covers an extended period from 01 May to 31 October 2022 (VAD products, 10-min averages) as this lidar was operated in a semi-permanent mode. 3. Instrument details General Measurements were taken with two scanning Doppler wind lidars, model Stream Line (SL88) and Stream Line XR (SLXR142), manufactured by HALO Photonics. The SL88 and SLXR142 are part of the Innsbruck Atmospheric Observatory (IAO; Karl et al. 2020). Available here are vertical profiles of radial velocity and backscatter data based on vertical stares at 1 Hz for the SL88 lidar and vertical profiles of horizontal winds (10-min averages) derived from plan position indicator (PPI) scans by applying the VAD method for the SLXR142 lidar. PPI scans were performed as continuous motion scans (CSM mode) at an azimuth angle of 70°. For continuous motion scans, the scanner moves continuously (changing its azimuth angle) while data is being acquired. Data correction No corrections were applied to the data (level0 data). 4. Data file structure File format Provided are data in netCDF format. File names contain date and time information in UTC. The following wildcard characters are used in the file examples below: yyyy - year; mm - month, dd - day; HH - hour, MM - minute, `SS` - second. NetCDF data files are zipped together into the following zip files. Zip files SL88.zip contains netCDF files of SL88 data structured into subdirectories (one subdirectory for each month, yyyymm, and one for each day, yyyymmdd). SLXR142.zip contains netCDF files of SLXR142 data structured into subdirectories (one subdirectory for each month, yyyymm). NetCDF files for uncorrected SL88 data Stare_88_yyyymmdd_HH_l0.nc contains vertical stare measurements aggregated together in one netCDF file for each hour (uncorrected level0 data). NetCDF files for SLXR142 data products yyyymmdd.nc contains vertical profiles of the horizontal wind vector derived from PPI scans by applying the VAD technique. Each vertical profile is based on several PPI scans conducted at an elevation angle of 70° within 10 minutes. Hence, each profile represents a 10-min average. Profiles are aggregated together for each day in a separate netCDF file. 6. Contact Contact alexander.gohm(at)uibk.ac.at for any questions regarding the data set. 7. References Karl, T., A. Gohm, M.W. Rotach, H.C. Ward, M. Graus, A. Cede, G. Wohlfahrt, A. Hammerle, M. Haid, M. Tiefengraber, C. Lamprecht, J. Vergeiner, A. Kreuter, J. Wagner, M. Staudinger, 2020: Studying urban climate and air quality in the Alps: The Innsbruck Atmospheric Observatory. Bulletin of the American Meteorological Society, 101, E488–E507, <a href="https://doi.org/10.1175/bams-d-19-0270.1">https://doi.org/10.1175/bams-d-19-0270.1</a> Serafin, S., M. W. Rotach, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. De Wekker, M. Evans, V. Grubi&scaron;ić, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Raudzens Bailey, J. Schmidli, G. Wohlfahrt, B. Zardi, 2020: Multi-scale transport and exchange processes in the atmosphere over mountains: Programme and experiment. Innsbruck University Press. <a href="https://doi.org/10.15203/99106-003-1">https://doi.org/10.15203/99106-003-1</a> Rotach, M. W., S. Serafin, H. C. Ward, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. D. Wekker, V. Grubi&scaron;ic, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Bailey, J.  Schmidli, C. Wittmann, G. Wohlfahrt, D. Zardi, 2022: A collaborative effort to better understand, measure, and model atmospheric exchange processes over mountains. Bulletin of the American Meteorological Society, 103, E1282–E1295. <a href="https://doi.org/10.1175/bams-d-21-0232.1">https://doi.org/10.1175/bams-d-21-0232.1</a>

opencc-by-4.0Jun 2023View details →

Dataset - Downscaling ERA5 Wind Speed Data: A Machine Learning approach considering Topographic Influences

This dataset provides three products: 1. The topographic data.   These data are provided as GeoTIFF files for Europe with 1km x 1km spatial resolution. These maps include: <ul> <li>Digital Elevation Model (DEM) map: Europe_DEM.tif</li> <li>Slope map: Europe_slope.tif</li> <li>Aspect map: Europe_aspect.tif</li> <li>Topographic Position Index (TPI) with a 5 km radius map: Europe_TPI_5.tif</li> <li>Topographic Position Index (TPI) with a 75 km radius map: Europe_TPI_75.tif</li> <li>Terrain Diversity Index (TDI) map: Europe_TDI.tif</li> </ul> These data can be used as input maps for the preprocessing step. In addition, the two TPI maps can also be used in the regression process. 2. The resulting map of the preprocessing step.  This map offers predictions on the quality of ERA5 data across Europe and is also provided as a GeoTIFF file with 1km x 1km spatial resolution under the name: <ul> <li> Europe_classification.tif</li> </ul> In this map, Class1 represents a good ERA5 quality with an RMSE of less than 1.5 m/s, Class2 represents a moderate ERA5 quality with an RMSE bigger than 1.5 m/s but less than 3 m/s, while Class 3 indicates a poor ERA5 quality with an RMSE greater than 3 m/s. 3. The downscaled wind speed time series data. Europe has been divided into 64 equal area blocks to accommodate the large data size. Each downscaled dataset is provided as a NetCDF file, offering hourly wind speed time series for a year (8760 hours) at approximately 1km x 1km spatial resolution. Each NetCDF file has three dimensions: 'lon' representing longitude, 'lat' representing latitude, and 'time' representing the hour. The variable name for wind speed in the NetCDF file is 'WindSpeed'. The 'WindSpeed' variable is stored as an Int32 data type in the NetCDF file, with values multiplied by 10000 in order to significantly reduce the data size. To utilize this variable, please divide it by 10000. For regions identified as Class1 and Class2, the downscaled wind speed is obtained through a simple nearest neighbour spatial interpolation of ERA5 due to the good quality of ERA5 in these regions. However, for the regions identified as Class3, the downscaled wind speed is derived using the machine learning-based regression approach described in the relevant publication. The geographic extent and the visual representation for each block are provided in 'Readme.pdf' document.   To cite this dataset, please cite our published paper in Environmental Research Letters (DOI: 10.1088/1748-9326/aceb0a)

opencc-by-4.0Aug 2023View details →

Björkö Wind Turbine Version 1 (45kW) high frequency Structural Health Monitoring (SHM) data

The Chalmers wind turbine has variable speed operation with a direct driven generator and a frequency converter, it also has a digital control system developed by Chalmers. The wind turbine has a rated power of 45 kW and rated speed of 75 rpm. The wooden tower is 30 m high, the blades of carbon fibres are 7.5 m long, and the turbine diameter is 15.9 m. The individually blade pitch system is electrical. The turbine is situated on the island Björkö at Skarviksvägen, 20 km west of Göteborg city. The coordinates are: 57.71818820625921, 11.683382148764485. 69 SCADA and structural vibration and loads Channels timeseries (sampled at 20 and 100 Hz) such as nacelle accelerations, tower and blades bending moments are included. Structured metadata about wind turbine characteristics, SCADA, vibration and loads channels are included as JSON files and CSV. This particular dataset consisting of high frequency sampled data, is intended for condition and structural health analysis. The data covers: <ul> <li>the measurements sampled at 100 Hz correspond to the period from 05 July 2022 to 9 June 2023</li> <li>the measurements sampled at 20 Hz correspond to the period from 05 July 2022 to 2 August 2023</li> </ul> This repository includes: Time-series data in csv format: <ul> <li>B1_CL4_20.csv (this is the data sampled at 20 Hz)</li> <li>B1_CL4_100.csv (this is the data sampled at 100 Hz)</li> </ul> Metadata: <ul> <li>Bjorko_Sensors_Specs_Metadata.csv (Sensors signals specification in csv format)</li> <li>Bjorko_modes_mapping.csv (numerical integer value representing the wind turbine controller system mode in csv format)</li> <li>Bjorko_modes_mapping.json (numerical integer value representing the wind turbine controller system mode in csv JSON format)</li> <li>Bjorko_digital_io_states_mappings.csv (Description of digital input and output states in the wind turbine controller system in csv format)</li> </ul> Media: <ul> <li>Chalmers-Wind turbine.pdf (description of the wind turbine including pictures)</li> <li>Chalmers wind turbine description 220121-short.pdf (description of the wind turbine including pictures)</li> </ul> Semantic artifacts: <ul> <li>N/A</li> </ul> Other: <ul> <li>N/A</li> </ul> Additional information is available upon request.

opencc-by-4.0Aug 2023View details →

Aventa AV-7 ETH Zurich Research Wind Turbine SCADA and high frequency Structural Health Monitoring (SHM) data

General description of wind turbine: The ETH owned wind turbine is Aventa AV-7, manufactured by Aventa AG in Switzerland and was commissioned in December 2002. The turbine is operated via a belt-driven generator and a frequency converter with a variable speed drive. The rated power of the Aventa AV-7 is 7 kW, beginning production at a wind speed of 2 m/s and having a cut-off speed of 14 m/s. The rotor diameter is 12.8 m with 3 rotor blades, and a hub height is 18m. The maximum rotational speed of the turbine is 63 rpm. The tower is a tubular steel-reinforced concrete structure, supported on concrete foundation, while the blades are made of glassfiber with a tubular steel main-spar. The turbine is regulated via a variable-speed and variable pitch control system. Location of site: The wind turbine is located in Taggenberg, about 5 km from the city centre of Winterthur, Switzerland. This site is easily accessible by public transport and on foot with direct road access right next to the turbine. This prime location reduces the cost of site visits and allows for frequent personal monitoring of the site when test equipment is installed. The coordinates of the site are: 47°31'12.2"N 8°40'55.7"E. Control and measurement systems and signals: The turbine is regulated via a variable-speed and collective variable pitch control system. SHM Motivation: Designed and commissioned in 2002, the Aventa wind turbine in Winterthur is soon reaching its end of design lifetime. In order to assess the various techniques of predicting the remaining useful lifetime, a Structural Health Monitoring (SHM) campaign was implemented by ETH Zurich. The monitoring campaign started in 2020, and is still ongoing. In addition, the setup is used as a research platform on topics such as system identification, operational modal analysis, faults/damage detection and classification. We analyze the influence of operational and environmental conditions on the modal parameters and to further infer Performance Indicators (PIs) for assessing structural behavior in terms of deterioration processes. Data Description: The tower and nacelle have been instrumented with 11 accelerometers distributed along the length of the tower, nacelle main frame, main bearing and generator. Two full bridge strain gauges are installed on the concrete tower based measuring fore-aft and side-side strain (and can be converted to bending moments) – all acceleration and strain signals sampled at 200Hz. Temperature and humidity are measured at the tower base – 1Hz data. In additional we are collecting operational performance data (SCADA), namely: wind speed, nacelle yaw orientation, rotor RPM, power output and turbine status – SCADA signals are sampled at 10Hz. See appendix for further details of the sensors layout. The measurements/instrumentation setup, type and layout is provided in the pdf files. The data: the data is provided in zip files corresponding to four use-cases as follows: <ul> <li>Normal operation data for system identification</li> <li>Aerodynamic imbalance on one blade</li> <li>Rotor icing event</li> <li>Failure of the flexible coupling of the linear drive of the collective pitch system</li> </ul> The data for each of the four uses-cases is organized in zip files. The content of each zip file is as follows: <ul> <li>Time-series data in HDF5 format</li> <li>Metadata: <ul> <li>Turbine specification (Aventa-AV-7.json and Aventa-AV-7.yaml)</li> <li>Sensor specification (Aventa_sensors.json )</li> <li>Unstructured description of the Aventa Turbine and the installed sensors (Aventa_Sensors_Specs.xlsx)</li> </ul> </li> <li>Semantic artifacts: <ul> <li>WindIO Wind Turbine YAML schema describing turbine specifications (IEAontology_schema.yaml)</li> <li>Sensor specification JSON schema (sensors_schema.json)</li> </ul> </li> <li>Media: Pictures of leading edge roughness and a clip of wind turbine operation</li> <li>Code: Jupyter notebook containing example code to load metadata from JSON and data from HDF5 files (example.ipynb)</li> </ul> Additional data is available upon request, please contact: <ul> <li>Prof. Dr. Eleni Chatzi (chatzi@ibk.baug.ethz.ch)</li> <li>Dr. Imad Abdallah (ai@rtdt.ai , abdallah@ibk.baug.ethz.ch)</li> </ul> For further details or questions, please contact: Prof. Dr. Eleni Chatzi Chair of Structural Mechanics & Monitoring ETH Zürich <a href="http://www.chatzi.ibk.ethz.ch/">http://www.chatzi.ibk.ethz.ch/</a>

opencc-by-4.0Aug 2023View details →

Data Sets: The Influence of Synoptic Wind on Land-Sea Breezes

DATA & FILE OVERVIEW This dataset contains the dimensional results of large-eddy simulations in .nc file format conducted over the thirty one simulations detailed in Allouche et al. (2023) (https://doi.org/10.1002/qj.4552) with differing synoptic pressure forcings and alignment angles of the latter with the shoreline. The patterns are altered as to conduct the analysis followed in Allouche et al. (2023) (https://doi.org/10.1002/qj.4552).

opencc-by-4.0Sep 2023View details →

Ice Throw from Wind Turbines: Experimental Data, 6DOF Model, CFD results, 3D Scans

Compiled data and code from the Eisball Project (funded by the Austrian Research Promotion Agency FFG, project number 865060)     6DOF_model_octave.zip - reference implementation of the six-degree-of-freedom model in MathML (Octave or MATLAB) experimental_data.csv - Experimental Data from dropping artificial ice fragments from wind turbines, recording drop distance and direction, details in experimental_data_column_description.txt ???_forces_and_moments.csv - forces and moments tables for the use in the 6DOF model, specific per specimen type   Data was first published in Nov 2021 at https://boku.ac.at/wau/risk/abgeschlossene-projekte/eisball-1 (may not persist)

openCC (other)Dec 2017View details →

High-frequency water temperature, chlorophyll fluorescence, wind speed, and photosynthetically active radiation data for 18 globally-distributed lakes 2008 - 2013

Abstract: This dataset was used in the analysis described in the manuscript by Rusak, J. A.J. Tanentzap, J.L. Klug, K. Rose, L.A. Winslow R. Smyth, E. Jennings, D. Pierson, S. Hendricks, A. Laas, E. Ryder, D. White, R. Adrian, L. Arvola, E. de Eyto, H. Feuchtmayr, M. Honti, V. Istanovics, I. Jones, C. McBride, S. Schmidt, G. Zhu. Wind and trophic status explain the temporal and spatial variability of chlorophyll in lakes. In review: Limnology and Oceanography Letters. The variation in chlorophyll fluorescence from 18 globally distributed lakes, was tested at monthly, daily and hourly scales in related to high-frequency measurements of wind, water temperature and radiation within lakes as well as lake productivity and morphometry among lakes. Overall, monthly variation in algal biomass was greater than that expressed at either daily or hourly scales but, combined, these latter time scales were equivalent to seasonal variation. Among lakes, algal biomass variation increased with trophic status while, within-lake variation increased with increasing wind speed variation. Together, our results suggest that predicted changes associated with a changing climate, as well as widespread ongoing cultural eutrophication, have the potential to substantially alter the variability of algal biomass and thus the predictability of the services it provides. This dataset includes the data used in the analysis described above.

openOpenDec 2015View details →

Daliy weather data (wind, temperatrue, humididty, pressure, precipitation) from Roche Mountonnee , in the northern foothills of the Brooks Range, Alaska, summers 2010-2014.

Daily weather data from mid May to late July 2011 to 2013 from Roche Moutonnee (south of Toolik Field Station and Arctic LTER), in the northern foothills of the Brooks Range, Alaska. Parameters measured include: wind speed, wind directions, temperature, humidity, pressure and precipitation.

openCC (other)Dec 2024View details →

Jornada Basin LTER Nutrient and Ecosystem impacts of Aeolian Transport Study (NEAT) Block 3 meteorological station: 5-minute summary wind and air temperature data: 2018 - ongoing

5-minute summary data at NEAT Block-3 met station. A met station consisting of a 10-meter mast is installed on the northwest corner of the site. Air temperature, wind direction and wind speed sensors are mounted on the mast. A vertical wind profile is measured with anemometers at 0. 45m, 0.90m, 1.90m, 4.40m and 10m on the mast. A wind vane is installed at 2.50m and 8.50m. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset.

openCC (other)Jan 2020View details →

Year 2018, PIE LTER wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) for 2018 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.

openCC (other)Jan 2020View details →

Year 2019, PIE LTER wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA

Wind sensor measurements (wind speed and wind direction) for 2019 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.

opencc-by-4.0May 2020View details →

Offshore wind competitiveness in mature markets without subsidy - Supplementary Data

This is the data set named "Supplementary Data 1" for the research paper "Offshore wind competitiveness in mature markets without subsidy". This data set also contains the raw data for reproducing Figure 1 through to Figure 4.  The paper is currently under review and access is for peer-review purposes only.

opencc-by-4.0Nov 2020View details →

Onshore & offshore WRF generated wind data

These data sets provide the WRF [1] calculated wind data for Pritzwalk (onshore) and FINO3 (offshore) as Python dictionaries.  Additionally, the files contain k-means cluster objects derived from these profiles. These data sets were used for power assessment and design exploration of Airborne Wind Energy Systems using the awebox [2] optimization toolbox.   WRF setups are described in detail and used in publication [3,4,5]. Wind data are interpolated to fixed heights of: [10,   28,   50,   70,   90,  100,  150,  200,  250,  300,  350, 400,  450,  500,  550,  600,   700,  800,  1000, 1200] meters above ground.   Onshore wind data:  <ul> <li> Location lat: 53° 10.78' N; long: 12° 11.35' E </li> <li> Time: 1 September 2015 - 31 August 2016 </li> <li> Timestep: 10 min </li> </ul> Offshore wind data:  <ul> <li> Location lat: 55° 11.7' N, long: 7° 9.5' E </li> <li> Time: 1 September 2013 - 31 August 2014 </li> <li> Timestep: 10 min </li> </ul>   The clusters are derived from both horizontal wind velocity components using the scikit-learn’s k-means clustering algorithm [6]. For our purposes, wind vectors were rotated such that the main wind speed always points in the same direction (u_main,u_deviation). [1]: <a href="https://www.mmm.ucar.edu/weather-research-and-forecasting-model"> Weather Research and Forecasting Model </a> [2]: <a href="https://github.com/awebox/awebox">awebox</a> [3]: <a href="https://doi.org/10.5194/wes-4-563-2019">Improving mesoscale wind speed forecasts using lidar-based observation nudging for airborne wind energy systems</a> [4]: <a href="https://doi.org/10.5194/wes-2020-120">Offshore and onshore ground-generation airborne wind energy power curve characterization </a> [5]:<a href="https://doi.org/10.5194/wes-2020-123">Ground-generation airborne wind energy design space exploration </a> [6]: <a href="https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html">sklearn.cluster.KMeans</a>

opencc-by-4.0Sep 2016View details →

X-ray CT data: fatigue damage in glass fibre/polyester composite used for wind turbine blades

These data are obtained using a Zeiss Xradia Versa 520 scanner to scan a uni-directional glass fibre reinforced polyester composite made from a non-crimp fabric used for wind turbine blades. The scans were performed to study the fatigue damage progression in this material. The data is published together with the below journal paper, in which more information can be found. The present videos of the data relate directly to the figures in this paper. Jespersen, K. M., Zangenberg Hansen, J., Lowe, T., Withers, P. J., & Mikkelsen, L. P. (2016). Fatigue damage assessment of uni-directional non-crimp fabric reinforced polyester composite using X-ray computed tomography. Composites Science and Technology, 136, 94–103. DOI:10.1016/j.compscitech.2016.10.006 For use of these data, please remember to cite the above mentioned paper. Corresponding author, K. M. Jespersen, e-mail kmun@dtu.dk

opencc-by-4.0Jan 2022View details →

Kelmarsh wind farm data

This dataset contains: <ul> <li>A kmz file for Kelmarsh wind farm in the UK (for opening in e.g. <a href="https://www.google.com/intl/en-GB/earth/versions/#earth-pro">Google Earth</a>)</li> <li>Static data including turbine coordinates and turbine details (rated power, rotor diameter, hub height, etc.)</li> <li>10-minute SCADA and events data from the 6 Senvion MM92's at Kelmarsh wind farm, grouped by year from 2016 to end 2024, which was extracted from Cubico's secondary SCADA system (Greenbyte). Note not all signals are available for the entire period</li> <li>Data mappings from primary SCADA to csv signal names</li> <li>Site substation/PMU meter data where available for the same period</li> <li>Site fiscal/grid meter data where available for the same period</li> </ul> The dataset has been released by <a href="https://www.cubicoinvest.com/">Cubico Sustainable Investments Ltd</a> under a <a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a> open data license and is provided as is. However, please provide any feedback you might have on the dataset and format of the data. Feel free to use the data according to the license, however, it would be helpful to me if you could let me know where, how and why you are using the data, so that I can highlight this to the business (and renewables industry) and hopefully promote similar data sharing initiatives. I am particularly interested in performance analysis/improvement opportunities, how the dataset can be augmented with other (open) datasets, and sharing more generally within the renewables industry. If you have any questions or want to discuss open data and this or other initiatives, please either: <ol> <li>Contact me on <a href="https://www.linkedin.com/in/charlie-plumley-8b8b613b">LinkedIn</a>, and I will endeavour to help</li> <li>Or in the <a href="https://www.wedowind.ch/">WeDoWind</a> platform in the ODE space</li> </ol> I would like to thank Cubico's Senior Legal Advisor & Compliance Officer, IT Director, UK Asset Management Team, Executive Committee and my manager and team for supporting this initiative, as well as our partners GLIL for agreeing to release this data under an open license. I would also like to thank those I have talked to during the process of releasing this data under an open license and the encouragement and advice I have had on the way. You can also access data from Penmanshiel wind farm <a href="https://doi.org/10.5281/zenodo.5946807">here</a>.

opencc-by-4.0Feb 2022View details →

Penmanshiel wind farm data

This dataset contains: <ul> <li>A kmz file for Penmanshiel wind farm in the UK (for opening in e.g. <a href="https://www.google.com/intl/en-GB/earth/versions/#earth-pro">Google Earth</a>)</li> <li>Static data including turbine coordinates and turbine details (rated power, rotor diameter, hub height, etc.)</li> <li>10-minute SCADA and events data from the 14 Senvion MM82's at Penmanshiel wind farm, grouped by year from 2016 to end of 2024, which was extracted from our secondary SCADA system (Greenbyte). Note not all signals are available for the entire period, and there is no turbine WT03</li> <li>Data mappings from primary SCADA to csv signal names</li> <li>Site substation/PMU meter data where available for the same period</li> <li>Site fiscal/grid meter data where available for the same period</li> </ul> The dataset has been released by <a href="https://www.cubicoinvest.com/">Cubico Sustainable Investments Ltd</a> under a <a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY-4.0</a> open data license and is provided as is. However, please provide any feedback you might have on the dataset and format of the data. Feel free to use the data according to the license, however, it would be helpful to me if you could let me know where, how and why you are using the data, so that I can highlight this to the business (and renewables industry) and hopefully promote similar data sharing initiatives. I am particularly interested in performance analysis/improvement opportunities, how the dataset can be augmented with other (open) datasets, and sharing more generally within the renewables industry. If you have any questions or want to discuss open data and this or other initiatives, please either: <ol> <li>Contact me on <a href="https://www.linkedin.com/in/charlie-plumley-8b8b613b">LinkedIn</a>, and I will endeavour to help</li> <li>Or in the <a href="https://www.wedowind.ch/">WeDoWind</a> platform in the ODE space</li> </ol> I would like to thank Cubico's Senior Legal Advisor & Compliance Officer, IT Director, UK Asset Management Team, Executive Committee and my manager and team for supporting this initiative, as well as our partners GLIL for agreeing to release this data under an open license. I would also like to thank those I have talked to during the process of releasing this data under an open license and the encouragement and advice I have had on the way. You can also access data from Kelmarsh wind farm <a href="https://doi.org/10.5281/zenodo.5841833">here</a>.

opencc-by-4.0Feb 2024View details →

Data for "Disrupted connectivity within a metapopulation of a wind-pollinated declining conifer Taxus baccata L."

A spreadsheet contains microsatellite genotypes and population coordinates necessary for estimating seed and pollen migration rates. In addition, a spreadsheet contains detailed individual data necessary for parentage analysis. For more details, see: Chybicki IJ, Robledo-Arnuncio JJ, Bodziarczyk J, Widlak M, Meyza, K, Oleksa A, Ulaszewski B (2024) Disrupted connectivity within a metapopulation of a wind-pollinated declining conifer, Taxus baccata L. Forest Ecosystems 100240 (https://www.sciencedirect.com/science/article/pii/S2197562024000769)

opencc-by-4.0Mar 2024View details →

Wind and SOLAR RES predicted production data for Crete and Peloponnese - ONENET WP8

WP8 aimed at the development and implementation of a web based app that enhances Active Power Management necessary for coordination of a TSOs and DSOs, using AI methods and cloud calculation engines that was tested in Peloponnese and Crete regions. Full description of the scope and results of WP8 Greek demo can be found in the relative deliverable D8.2: Development and implementation of the “F-Channel” platform (https://www.onenet-project.eu//wp-content/uploads/2023/10/OneNet_D8.2_V1.0.pdf). For purpose of this project similar, historical weather data in 1 hour resolution have been used in order to obtain behavior patterns of climatic parameters (daily, monthly, season) throughout region of interest. For this purpose various ERA5 climatic datasets has been used and AI algorithms applied in combination with terrain orography data. Modeled results was compared with operational data from TSO/DSO and appropriate model calibration has been provided based on deep learning AI algorithms. The data for Wind power plant modelled production is given in file <<a href="../api/records/10848817/draft/files/wind_res_onenet_wp8.csv/content" target="_blank" rel="noopener noreferrer">wind_res_onenet_wp8.csv</a>> in the following columns <time> ; <aa> ; <pw> ; <ws> ; <wp_name> . Columns are related to: Hourly time, wind park code, power [MWh], wind speed [m/s] and wind park name, respectively. The data for Solar power plant modelled production is given in file <<a href="../api/records/10848817/draft/files/solar_onenet_wp8.csv/content" target="_blank" rel="noopener noreferrer">solar_onenet_wp8.csv</a>> in the following columns <time> ; <name> ; <pw> ; <ta> ; <ghi> . Columns are related to: Hourly time, solar park name, power in MWh, ambient temperature and Global Horizontal irradiance [W/m2], respectively.

opencc-by-4.0Apr 2024View details →

Source data for "Halving the North Sea's offshore wind energy carbon footprint"

This dataset provides source data for the paper "Halving the North Sea’s offshore wind energy carbon footprint". It contains basic geographical factors, including wind speed, water depth, and distance from shore, and environmental impact intensities, including steel, Cu, and Al use, climate change, marine ecotoxicity, and marine eutrophication impacts. For more details, please refer to https://pubs.acs.org/doi/full/10.1021/acs.est.2c02183 and https://www.sciencedirect.com/science/article/pii/S1364032122004993.