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16 results for “offshore wind farms”
Data supplement for "Alignment of scanning lidars in offshore wind farms" - Wind Energy Science Journal
<p>These data are supplements for the calculations of the methods from the article "Alignment of scanning lidars in offshore wind farms".<br> The data was used to produce the results from the publication and is intended to be used here as sample data for illustrative purposes.</p>
Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design
<p>Dataset related to the article: Virtanen, E.A., Lappalainen, J., Nurmi, M., Viitasalo, M., Tikanmäki, M., Heinonen, J., Atlaskin, E., Kallasvuo, M., Tikkanen, H., Moilanen, A. (2022) Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design. Renewable and Sustainable Energy Reviews 158, 112087.</p> <p>Dataset includes suitability maps for offshore windfarms, where priority values are scaled between 0-1 (note the reversed value scale): analysis solution (A) economy, (B) society, (C) biodiversity, (D) restrictions, (E) A+B+C without restrictions and (F) A+B+C with restrictions. Dataset includes also the conflict map (and R script), where each three main solutions (A, B, C) are mapped onto an RGB color composite map. </p> <p>Additional details can be found from the published article: <a href="https://doi.org/10.1016/j.rser.2022.112087">https://doi.org/10.1016/j.rser.2022.112087</a></p>
Comparison of Large Eddy Simulations against measurements from the Lillgrund offshore wind farm - Manuscript data
<p>Time averaged power and farm inflow velocity for the manuscript "Comparison of Large Eddy Simulations against measurements from the Lillgrund offshore wind farm" for publication in the wind energy science journal. Data is uploaded for the 5 simulation cases covered.</p> <p>'Power' files contain average power production for 48 turbines. First row corresponds to LES data, second row corresponds to SCADA data from the Lillgrund wind farm.</p> <p>'Velocity' files contain inflow mean velocity measurements at the 72 range gate locations. First row corresponds to LES inflow data, second row corresponds to LIDAR inflow data from the Lillgrund wind farm.</p>
Dataset for "Power curve estimation with multivariate environmental factors for inland and offshore wind farms"
<p>This is the dataset used in the paper, Lee, Ding, Genton, and Xie, 2015, “Power curve estimation with multivariate environmental factors for inland and offshore wind farms,” <em>Journal of the American Statistical Association</em>, Vol. 110, pp. 56-67.</p>
European offshore wind farms and marine energy deployements
<p>Three distinct dataset used to forecast the development of marine energy in Europe in the upcoming three decades:</p> <p>- European offshore wind farms</p> <p>- tidal energy converter deployements in Europe</p> <p>- wave energy converter deployements in Europe</p>
Techno-economic evaluation and resource assessment of hydrogen production through offshore wind farms: A European perspective - Supplementary material
<p>This is the additional material provided with the journal article "Techno-economic evaluation and resource assessment of hydrogen production through offshore wind farms: A European perspective" published in Renewable and Sustainable Energy Reviews (<a href="https://doi.org/10.1016/j.rser.2023.113699">https://doi.org/10.1016/j.rser.2023.113699</a>).</p> <p>Datasets are provided as NetCDF files for European maps and CSVfor Economically Attractive Resource curves.</p> <p>European and National plots are provided as PDF files.</p>
Avoidance of offshore wind farms by Sandwich Terns increases with turbine density
<p>The expanding use of wind farms as a source of renewable energy can impact bird populations due to collisions and other factors. Globally, seabirds are one of the avian taxonomic groups most threatened by anthropogenic disturbance; adequately assessing the potential impact of offshore wind farms (OWFs) is important for developing strategies to avoid or minimize harm to their populations. We estimated avoidance rates of OWFs — the degree to which birds show reduced utilization of OWF areas — by Sandwich Terns <em>Thalasseus sandvicensis</em> at two breeding colonies in western Europe: Scolt Head (United Kingdom) and De Putten (the Netherlands). We modeled GPS tracking data using integrated Step Selection Functions (iSSFs) to estimate the relative selection of habitats at the scale of time between successive GPS relocations – in our case 10 minutes, in which terns traveled ca. 2 km on average. The foraging ranges of birds from each colony overlapped with multiple OWFs. iSSFs considered distance from the colony and habitat characteristics (water depth and sediment grain size) and movement characteristics. Macro-avoidance rates, where 1 means complete avoidance, were estimated at 0.54 (95% CrI = 0.35, 0.7) for birds originating from Scolt Head and 0.41 (95% CrI = 0.21, 0.56) for those from De Putten. Estimates for individual OWFs also indicated avoidance but were associated with considerable uncertainty. Our results were inconclusive with regard to the behavioral response to the areas directly surrounding OWFs (within 1.5 km); estimates suggested indifference and avoidance and were associated with large uncertainty. Avoidance rate of OWFs significantly increased with turbine density, suggesting OWF design may help to reduce the impact of OWFs on Sandwich Terns. The partial avoidance of OWFs by Sandwich Terns implies that the species will experience risks of collision and habitat loss due to OWFs constructed within their foraging ranges.</p>
Avoidance of offshore wind farms by Sandwich Terns increases with turbine density
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Data from: Three dimensional tracking of a wide-ranging marine predator: flight heights and vulnerability to offshore wind farms
A large increase in offshore wind turbine capacity is anticipated within the next decade, raising concerns about possible adverse impacts on birds as a result of collision risk. Birds' flight heights greatly influence this risk, yet height estimates are currently available only using methods such as radar- or ship-based observations over limited areas. Bird-borne data-loggers have the potential to provide improved estimates of collision risk and here, we used data from Global Position System (GPS)-loggers and barometric pressure loggers to track the three-dimensional movements of northern gannets rearing chicks at a large colony in south-east Scotland (Bass Rock), located <50 km from several major wind farm developments with recent planning consent. We estimated the foraging ranges and densities of birds at sea, their flight heights during different activities and the spatial variation in height during trips. We then used these data in collision-risk models to explore how the use of different methods to determine flight height affects the predicted risk of birds colliding with turbines. Gannets foraged in and around planned wind farm sites. The probability of flying at collision-risk height was low during commuting between colonies and foraging areas (median height 12 m) but was greater during periods of active foraging (median height 27 m), and we estimated that ˜1500 breeding adults from Bass Rock could be killed by collision with wind turbines at two planned sites in the Firth of Forth region each year. This is up to 12 times greater than the potential mortality predicted using other available flight-height estimates. Synthesis and applications: The use of conventional flight-height estimation techniques resulted in large underestimates of the numbers of birds at risk of colliding with wind turbines. Hence, we recommend using GPS and barometric tracking to derive activity-specific and spatially explicit flight heights and collision risks. Our predictions of potential mortality approached levels at which long-term population viability could be threatened, highlighting a need for further data to refine estimates of collision risks and sustainable mortality thresholds. We also advocate raising the minimum permitted clearance of turbine blades at sites with high potential collision risk from 22 to 30 m above sea level.
Offshore wind farms low-trophic aquaculture multi-use potential: Figure data
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Data from: Three dimensional tracking of a wide-ranging marine predator: flight heights and vulnerability to offshore wind farms
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Data from: Sound exposure in harbour seals during the installation of an offshore wind farm: predictions of auditory damage
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Data from: A global review of Procellariiform flight height, flight speed and nocturnal activity: Implications for offshore wind farm collision risk
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Inland-Offshore Wind Farm Dataset2
<p>The wind turbine data in these two datasets include observations during the first four years of the turbines' operations. They are 10-minute data. The inland turbine data are from 2008 to 2011, whereas the offshore data are from 2007 to 2010. The measurements for the inland wind farm include the same x's as in the <a href="https://zenodo.org/record/5516552">Inland Wind Farm Dataset1</a> and those for the offshore wind farm include the same x's as in the <a href="https://zenodo.org/record/5516552">Offshore Wind Farm Dataset1</a>. Most of the environmental measurements are taken from the met mast closest to the turbine, with the exception of wind speed and turbulence intensity which are measured on the wind turbine. The mast measurements are used either because some variables are only measured at the mast (such as air pressure and ambient temperature, which are used to calculate air density) or because the mast measurements are considered more reliable (such as wind direction).</p>
Inland-Offshore Wind Farm Dataset1
<p>Data included in these two datasets are 10-minute data generated from six wind turbines and three met masts and are arranged in six files, each of which is associated with a turbine. The six turbines are named WT1 through WT6, respectively. The layout of the turbines and the met masts is shown in Fig. 5.6 of the <a href="https://aml.engr.tamu.edu/book-dswe/">Data Science for Wind Energy</a> book. On the offshore wind farm, all seven environmental variables as mentioned above are available, namely x =(V, D, rho, H, I, Sa, Sb), whereas on the inland wind farm, the humidity measurements are not available, nor is the above-hub wind shear, meaning that x =(V, D, rho, I, Sb). Variables in x were measured by sensors on the met mast, whereas y was measured at the wind turbines. Each met mast has two wind turbines associated with it, meaning that the x's measured at a met mast are paired with the y's of two associated turbines. For WT1 and WT2, the data were collected from July 30, 2010 through July 31, 2011 and for WT3 and WT4, the data were collected from April 29, 2010 through April 30, 2011. For WT5 and WT6, the data were collected from January 1, 2009 through December 31, 2009.</p> <p>Meaning of variables; V: wind speed; D: wind direction; rho: air density; H: humidity; I: turbulence intensity; S: vertical wind shear; Sa: above-hub height wind shear, Sb: below-hub height wind shear.</p>
Towed chain datasets and input files for simulations used in the manuscript "Increased mixing and turbulence in the wake of offshore wind farm foundations"
<p><strong>Contents</strong></p> <p>1. File S01_S12 Input files for simulations (precursor runs and main runs)</p> <p>2. File S13 Topography file for simulations with monopile</p> <p>2. Data sets ds01 to ds06 (towed chain data collected in May 25, 2015)</p> <p>3. Data sets ds07 to ds14 (towed chain data collected in July 19, 2017)</p> <p>4. Data sets d15 to ds16 (ADCP data collected in May 25, 2015 and July 19, 2017)</p> <p><strong>Introduction </strong></p> <p>This package contains the input parameters used in each of the precursor (S01 - S04) and main runs (S05 - S12) presented in the manuscript “Increased mixing and turbulence in the wake of offshore wind farm foundations”. These input files are found in the PDF file "S01_S12".</p> <p>The main runs, in which the wake of a monopile was simulated (S05, S07, S09, S11), require a topography file, which is a NETCDF-file that has been uploaded separately (S13). All simulations were run using the Parallelized Large-Eddy Simulation Model for atmospheric and oceanic flows (PALM, version 4.0, revision 2504).</p> <p>Further, this package contains the data sets collected using the towed chain in 2015 (ds01-ds06, ds15) and 2017 (ds07-ds14, ds16), which have been uploaded as separate NETCDF-files.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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