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33 results for “Time in power”
Capacity factor time series for solar and wind power on a 50 km^2 grid in Europe
<p>This spatio-temporal dataset contains capacity factors timeseries for locations on a grid with 50km edge length in Europe. The data is resolved in one hour timesteps and comprises the years 2000--2016. It has been generated using <a href="https://www.renewables.ninja">Renewables.ninja</a> and is based on MERRA-2 reanalysis data. For each of the ~2700 onshore location, it contains one time series for onshore wind turbines and five time series for PV installations with different orientations and tilts. PV time series exist for (1) installations on open fields, (2) installations on all possible rooftops, (3) south-facing and flat rooftops, (4) east- and west-facing rooftops, (5) north-facing rooftops. For each of the ~2800 offshore location there is one timeseries for offshore wind turbines.</p> <p>Two GeoTIFF files contain spatial information of onshore and offshore locations. For each of the three technologies -- onshore wind, offshore wind, and PV -- there is one NetCDF file determining the temporal dimension and containing the data. The GeoTIFF and NetCDF files are linked through unique IDs for all locations.</p> <p>This data serves as input data to euro-calliope, a model of the European electricity system.</p> <p>The following parameters have been used to generate the timeseries:</p> <pre><code>resolution-grid: 50 # [km^2] corresponding to MERRA resolution pv-performance-ratio: 0.9 hub-height: onshore: 105 # m, median hub height of V90/2000 in Europe between 2010 and 2018 offshore: 87 # m, median hub height of SWT-3.6-107 in Europe between 2010 and 2018 turbine: onshore: "vestas v90 2000" # most built between 2010 and 2018 in Europe offshore: "siemens swt 3.6 107" # most built between 2010 and 2018 in Europe</code></pre> <p>CHANGELOG:</p> <p>Version 3 (2022-05-18)</p> <p>* Update spatial scope to include Iceland and its offshore EEZ.<br> * Update temporal scope to include 2017 and 2018.</p> <p>Effect of increasing spatial scope is a slight change in the spatial position of the data points.</p> <p>Version 2 (2020-06-18)</p> <p>* Add time series for rooftop PV with different orientations.</p>
Solar and interplanetary magnetic field data analyzed in "Optimal frequency-domain analysis for spacecraft time series: Introducing the missing-data multitaper power spectrum estimator"
<p>This dataset contains simultaneous measurements of the interplanetary magnetic field magnitude <B> and the sun's radio flux at 10.7 cm <F10.7>. <B> measurements come from a series of spacecraft located at the L1 point, while <F10.7> was measured by the ongoing monitoring program by Canada's Dominion Radio Astrophysical Observatory. Bartels rotation-averaged data were downloaded from NASA's OMNIWeb, https://omniweb.gsfc.nasa.gov/html/ow_data.html. The file contains other solar wind plasma parameters that were not used in the analysis.</p>
Data and Code for: Real-Time Pricing and the Cost of Clean Power
<p>Solar and wind power are now cheaper than fossil fuels but are intermittent. The extra supply-side variability implies growing benefits of using real-time retail pricing (RTP). We evaluate the potential gains of RTP using a model that jointly solves investment, supply, storage, and demand to obtain a chronologically detailed dynamic equilibrium for the island of Oahu, Hawai'i. We find that RTP reduces costs in high-renewable systems by roughly 6 to 12 times as much as in fossil systems holding demand assumptions fixed, markedly lowering the cost of clean energy integration.</p>
USENIX'24 Artifact Datasets: With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors
<p>This dataset contains the measurements and analysis results for our USENIX Security '24 paper 'With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors'.</p>
Can a knee sleeve influence ground reaction forces and knee joint power during a step-down hop in participants following ACL reconstruction? Discrete and time-continuous datasets
<p>Using a cross-over design, we estimated GRF and knee kinematics and kinetics during a step-down hop for 30 participants (age 26.1 [SD 6.7] years, 14 women) following ACL reconstruction (median 16 months post-surgery) with and without wearing a knee sleeve. In a subsequent randomised clinical trial, participants in the ‘Sleeve Group’ (n=9) then wore the sleeve for 6 weeks at least 1 hour daily, while a ‘Control Group’ (n=9) did not wear the sleeve. Statistical parametric mapping (SPM) was used to compare (1) GRF trajectories in the three planes as well as knee joint power between three conditions at baseline (uninjured side, unsleeved injured and sleeved injured side); (2) within-participant changes for GRF and knee joint power trajectories from baseline to follow-up between groups. We also compared discrete peak GRFs and power, rate of (vertical) force development, and mean knee joint power in the first 5% of stance phase. Time-continuous and discrete data are included in this dataset.</p>
Transient analysis of power loss density with time-harmonic electromagnetic waves in Debye media
<p>Due to the complex permittivity, it is difficult to directly clarify the transient mechanism between electromagnetic waves and Debye media. To overcome above problem, the temporal relationship between the electromagnetic waves and permittivity is explicitly derived by applying the Fourier inversion and introducing the remnant displacement. With the help of the Poynting theorem and energy conservation equation, the transient power loss density is derived to describe the transient dissipation of electromagnetic field and the mechanism on phase displacement has been explicitly revealed. Besides, the unique solution can be obtained by applying the time-domain analysis method rather than involving the frequency-domain characteristics. The effectiveness of transient analysis is demonstrated by giving a comparison simulation on one-dimensional example.</p>
Real-Time Adaptation of an Artificial Neural Network for Transfemoral Amputees Using a Powered Prosthesis
<p>This dataset contains the data used in our manuscript titled "Real-Time Adaptation of an Artificial Neural Network for Transfemoral Amputees Using a Powered Prosthesis". Data structure is explained in the README.txt file located at the top-level of the dataset.</p> <p>Please contact corresponding author Richard B. Woodward for any questions.</p>
Transcranial direct current stimulation (tDCS) over the left prefrontal cortex does not affect time-trial self-paced cycling performance: Evidence from oscillatory brain activity and power output.
<p>This research will shed new light into the bidirectional relationship between acute aerobic exercise, brain and cognition. This is based on the particular role of executive (cognitive) function during exercise. The rationale of our study is that stimulation of the prefrontal cortex that has been repeatedly associated with executive function, would facilitate or impair self-paced aerobic exercise. This would also affect cognitive performance immediately after exercise. We will use a modified flanker’s task as a form of assessing executive function (see below for further details). The flanker’s task implies two different stimuli, one congruent and one incongruent. Relative to “congruent” stimuli, these “incongruent” stimuli are usually accompanied by increased response times (RTs) and decreased accuracy. To stimulate the prefrontal cortex, we use transcranial direct-current stimulation (tDCS). tDCS is able to induce cortical changes by hyperpolarizing (anodal) or depolarizing (cathodal) neuron’s resting membrane potential.<br> Therefore, the hypotheses of this research are:<br> 1) Anodal stimulation (relative to sham and cathodal stimulation) will improve self-paced aerobic exercise and, consequently it will also improve subsequent cognitive performance.<br> 2) Cathodal stimulation (relative to sham and anodal stimulation) will impair self-paced aerobic exercise and subsequent cognitive performance.<br> </p>
Bias-corrected simluated wind power generation time series for Brazil
<p>Simulated and bias corrected wind power generation time series data sets for Brazil, its North-East and South, seven states and seven wind parks.</p> <p>The data sources, generation and validation of the datasets are described in the article "Assessing the Global Wind Atlas and local measurements for bias correction of wind power generation simulated from MERRA-2 in Brazil", preprint available on arXiv: arxiv.org/abs/1904.13083, final version DOI: <a href="https://doi.org/10.1016/j.energy.2019.116212">10.1016/j.energy.2019.116212</a></p> <p>Code for generating the datasets is available at github.com/KatharinaGruber/BrazilWindpower_biascorr</p> <p> </p> <p>The files "comp_*" contain comparisons of simulated and observed wind power generation time series with daily resolution for all regions.</p> <p>"comp_noc.RData" is for comparison of interpolation methods and contains time series generated with Nearest Neighbour interpolation (NN), Bilinear Interpolation (BLI) and Inverse Distance Weighting (IDW).</p> <p>"comp_wmsa.RData" is for comparison of wind speed mean approximation methods and contains time series generated with Nearest Neighbour interpolation (NN - no correction applied), mean approximation with measured data (IN) and mean approximation with the Global Wind Atlas (GWA).</p> <p>"comp_wsc.RData" is for comparison of spatiotemporal wind speed correction methods and contains time series generated with mean approximation with the Global Wind Atlas (wmsa) and combined mean approximation with the Global Wind Atlas and hourly and monthly mean approximation with measured data (wschm).</p> <p> </p> <p>The files "statpowlist_*" contain hourly simulated wind power generation time series for three interpolation methods (NN, BLI, IDW), two mean approximation methods (wsmaIN - measured data (INMET), wsmaWA - Global Wind Atlas) as well as for spatiotemporal (hourly and monthly) wind speed bias correction (wschm) for each wind park available in The Wind Power dataset.</p>
Pre-Processed Power Grid Frequency Time Series
<p><strong>Overview</strong><br> This repository contains ready-to-use frequency time series as well as the corresponding pre-processing scripts in python. The data covers three synchronous areas of the European power grid:</p> <ul> <li>Continental Europe</li> <li>Great Britain</li> <li>Nordic</li> </ul> <p>This work is part of the paper "Predictability of Power Grid Frequency"[1]. Please cite this paper, when using the data and the code. For a detailed documentation of the pre-processing procedure we refer to the supplementary material of the paper.</p> <p><strong>Data sources</strong><br> We downloaded the frequency recordings from publically available repositories of three different Transmission System Operators (TSOs).</p> <ul> <li><strong>Continental Europe </strong>[2]: We downloaded the data from the German TSO <em>TransnetBW GmbH,</em> which retains the Copyright on the data, but allows to re-publish it upon request [3].</li> <li><strong>Great Britain </strong>[4]: The download was supported by National Grid ESO Open Data, which belongs to the British TSO <em>National Grid</em>. They publish the frequency recordings under the NGESO Open License [5].</li> <li><strong>Nordic</strong> [6]: We obtained the data from the Finish TSO <em>Fingrid</em>, which provides the data under the open license CC-BY 4.0 [7].</li> </ul> <p><strong>Content of the repository</strong></p> <p><strong>A) Scripts</strong></p> <ol> <li>In the "Download_scripts" folder you will find three scripts to automatically download frequency data from the TSO's websites.</li> <li>In "convert_data_format.py" we save the data with corrected timestamp formats. Missing data is marked as NaN (processing step (1) in the supplementary material of [1]).</li> <li>In "clean_corrupted_data.py" we load the converted data and identify corrupted recordings. We mark them as NaN and clean some of the resulting data holes (processing step (2) in the supplementary material of [1]).</li> </ol> <p>The python scripts run with Python 3.7 and with the packages found in "requirements.txt".</p> <p><strong>B) Yearly converted and cleansed data</strong><br> The folders "<year>_converted" contain the output of "convert_data_format.py" and "<year>_cleansed" contain the output of "clean_corrupted_data.py".</p> <ul> <li><strong>File type</strong>: The files are zipped csv-files, where each file comprises one year.</li> <li><strong>Data format</strong>: The files contain two columns. The second column contains the frequency values in Hz. The first one represents the time stamps in the format <em>Year-Month-Day Hour-Minute-Second</em>, which is given as naive local time. The local time refers to the following time zones and includes Daylight Saving Times (python time zone in brackets): <ul> <li>TransnetBW: Continental European Time (<em>CE)</em></li> <li>Nationalgrid: Great Britain (<em>GB</em>)</li> <li>Fingrid: Finland (<em>Europe/Helsinki</em>)</li> </ul> </li> <li><strong>NaN representation</strong>: We mark corrupted and missing data as "NaN" in the csv-files.</li> </ul> <p><strong>Use cases</strong><br> We point out that this repository can be used in two different was:</p> <ul> <li><strong>Use pre-processed data</strong>: You can directly use the converted or the cleansed data. Note however, that both data sets include segments of NaN-values due to missing and corrupted recordings. Only a very small part of the NaN-values were eliminated in the cleansed data to not manipulate the data too much.</li> </ul> <ul> <li><strong>Produce your own cleansed data</strong>: Depending on your application, you might want to cleanse the data in a custom way. You can easily add your custom cleansing procedure in "clean_corrupted_data.py" and then produce cleansed data from the raw data in "<year>_converted".</li> </ul> <p><strong>License</strong></p> <p>This work is licensed under multiple licenses, which are located in the "LICENSES" folder.</p> <ul> <li>We release the code in the folder "Scripts" under the MIT license .</li> <li>The pre-processed data in the subfolders "**/Fingrid" and "**/Nationalgrid" are licensed under CC-BY 4.0.</li> <li>TransnetBW originally did not publish their data under an open license. We have explicitly received the permission to publish the pre-processed version from TransnetBW. However, we cannot publish our pre-processed version under an open license due to the missing license of the original TransnetBW data.</li> </ul> <p><strong>Changelog</strong><br> Version 2:</p> <ul> <li>Add time zone information to description</li> <li>Include new frequency data</li> <li>Update references</li> <li>Change folder structure to yearly folders</li> </ul> <p>Version 3:</p> <ul> <li> <p>Correct TransnetBW files for missing data in May 2016</p> </li> </ul>
Figures of Influence of Time-Varying Atmospheric Channel on Time Reversal Spatial Power Combination of Sparse Array on Ground
<p>A mathematical model of TR (Time Reversal) power combination in the slowly time-varying atmospheric channel and the concept of channel time-varying factor are proposed to study the feasibility of TR technique for spatial power combination in incompletely reciprocal channels. Combining the theoretical analysis and Monte Carlo simulations, we analyze the relationship between the power combination efficiency and the permittivity of the time-varying atmospheric channel, which follows a normal distribution.</p> <p>In this data file, simulation results obtained by commercial software MATLAB are listed. The results indicate that the power combination efficiency of the TR technique will be lower than that of the completely reciprocal channel if the atmospheric channel is slowly varied, and the faster the channel changes, the lower the efficiency becomes. In order to keep the power combination efficiency above 50%, the channel time-varying factor needs to be less than about 54.3% of the signal period. And when it takes more than 90%, the signals radiated by each antenna have been completely incoherent at target point.</p>
Transient analysis of power loss density with time-harmonic electromagnetic waves in Debye media
Open the record for dataset details and reuse information.
Machine-learning-long-term-wind-power-time-series
<p>In this research work we assess how time-series generated by machine learning models (MLM) compare to Renewables.ninja in terms of their ability to replicate the characteristics of observed nationally aggregated wind power generation for Germany. With this archive, we want to make three machine learning derived time-series available in the Feather format for everyone interested.</p>
High-resolution wind power generation time series for Germany in the period 2000-2015
<p>High-resolution wind power generation time series for Germany in the period 2000-2015. A paper describing the applied methodology can be found in this repository as well.</p> <p>The final temporal resolution is hourly and the spatial resolution NUTS 3. The data is stored in csv and hdf5 files.</p> <p>More information on the data set, e.g. missing time stamps and versioning, can be found in readme.txt.</p>
The dataset of dump recordings of power DLS spectra annotated with the actual flow of intralipid for every moment of time
<p>Raw data in this dataset was measured in a biotissue phantom with a capillary channel where the intralipid was pumped with the flow (in ul/min) monitored by a precise flowmeter. The dataset presents dump recordings of power spectra from the photodetector annotated with the actual flow of intralipid for every moment of time for different depths of the capillary. The data was used to build the regression model which has been used to solve the inverse problem of the RBC velocity distribution assessment and for instant calculation of blood flow components from different depths. </p>
Real-Time Motor Unit Tracking from sEMG Signals with Adaptive ICA on a Parallel Ultra-Low Power Processor
<p>Dataset and code to replicate the paper:</p> <p>Orlandi et al., "Real-Time Motor Unit Tracking from sEMG Signals with Adaptive ICA on a Parallel Ultra-Low Power Processor"</p>
One year time series of relative electric (onshore and offshore) wind turbine power datasets
<p>The datasets (in dat file format) contain ordered time series (in unit of hours with 15 minutes time resolution) of relative electric wind turbine (WT) power (expressed in percentage) of one randomly selected year (05 August 2022 to 04 August 2023) and four of its constituting weeks (01 to 07 SEP 2022, 02 to 08 JAN 2023, 13 to 19 MAR 2023 and 16 to 22 JUL 2023) with their associated graphs (in PNG file format). The original data stem from the electricity grid of Flanders (onshore) and Belgium (offshore) as provided by Elia ( <a href="https://priv-lu-myremote.tech.ec.europa.eu/en/grid-data/power-generation/,DanaInfo=.awxyCiqohHko,SSL+solar-pv-power-generation-data">https://www.elia.be/en/grid-data/power-generation/solar-pv-power-generation-data</a> ) under CC BY 4.0 license (<a href="https://priv-lu-myremote.tech.ec.europa.eu/en/grid-data/,DanaInfo=.awxyCiqohHko,SSL+elia-open-data-license?csrt=16568311101247852187">https://www.elia.be/en/grid-data/elia-open-data-license?csrt=16568311101247852187</a>). The relative electric WT power was derived by dividing the measured electric WT power by the monitored peak electric WT power multiplied by 100 %.</p>
One year time series of relative electric photovoltaic power dataset
<p>The datasets (in dat file format) contain ordered time series (in unit of hours with 15 minutes time resolution) of relative electric photovoltaic (PV) power (expressed in percentage) of one randomly selected year (05 August 2022 to 04 August 2023) and four of its constituting weeks (01 to 07 SEP 2022, 02 to 08 JAN 2023, 13 to 19 MAR 2023 and 16 to 22 JUL 2023) with their associated graphs (in PNG file format). The original data stem from the electricity grid of Brussels as provided by Elia ( <a href="https://priv-lu-myremote.tech.ec.europa.eu/en/grid-data/power-generation/,DanaInfo=.awxyCiqohHko,SSL+solar-pv-power-generation-data">https://www.elia.be/en/grid-data/power-generation/solar-pv-power-generation-data</a> ) under CC BY 4.0 license (<a href="https://priv-lu-myremote.tech.ec.europa.eu/en/grid-data/,DanaInfo=.awxyCiqohHko,SSL+elia-open-data-license?csrt=16568311101247852187">https://www.elia.be/en/grid-data/elia-open-data-license?csrt=16568311101247852187</a>). The relative electric PV power was derived by dividing the measured electric PV power by the monitored peak electric PV power multiplied by 100 %.</p>
Data from: Temporal power of a cycling sprinter: Experiments & effective time theory
Open the record for dataset details and reuse information.
Weather- and climate-driven power supply and demand time series for European countries
<p>This repository contains times series of wind power generation, solar power generation, hydropower inflow, heating demand, and cooling demand that use both historical and projected climate variables. Historical climate data are from the ERA5 reanalysis (1940-2023), while projected climate variables are from three climate models of the CMIP5 EURO-CORDEX (2006-2100) and three representative concentration pathways (RCP 2.6, RCP 4.5 and RCP 8.5). The time series are at country level for the EU27 countries, Balkan countries, the United Kingdom, Norway, and Switzerland.</p> <p>Each country has the following files for the historical period, where ** is the country’s ISO Alpha-2 code:</p> <p>1) **__ERA5__wind__capacity_factor_time_series__onshore.nc<br>2) **__ERA5__wind__capacity_factor_time_series__offshore.nc<br>3) **__ERA5__solar__capacity_factor_time_series.nc<br>4) **__ERA5__hydropower__inflow_time_series__conventional_and_pumped_storage.nc<br>5) **__ERA5__hydropower__inflow_time_series__run_of_river.nc<br>6) **__ERA5__heating__demand_time_series__residential_space.nc<br>7) **__ERA5__heating__demand_time_series__services_space.nc<br>8) **__ERA5__cooling__demand_time_series.nc</p> <p>Each country has also the following files for the climate projections, where ** is the country’s ISO Alpha-2 code, #_# is the value of the RCP scenario (e.g., 2_6 is RCP 2.6), ££££__&&&& is the name of the combination of global-regional climate models (e.g., CNRM_CERFACS_CM5__CNRM_ALADIN63):</p> <p>9) **__CORDEX__RCP_#_#__££££__&&&&__wind__capacity_factor_time_series__onshore.nc<br>10) **__CORDEX__RCP_#_#__££££__&&&&__wind__capacity_factor_time_series__offshore.nc<br>11) **__CORDEX__RCP_#_#__££££__&&&&__solar__capacity_factor_time_series.nc<br>12) **__CORDEX__RCP_#_#__££££__&&&&__hydropower__inflow_time_series__conventional_and_pumped_storage.nc<br>13) **__CORDEX__RCP_#_#__££££__&&&&__hydropower__inflow_time_series__run_of_river.nc<br>14) **__CORDEX__RCP_#_#__££££__&&&&__heating__demand_time_series__residential_space.nc<br>15) **__CORDEX__RCP_#_#__££££__&&&&__heating__demand_time_series__services_space.nc<br>16) **__CORDEX__RCP_#_#__££££__&&&&__cooling__demand_time_series.nc</p> <p>The repository also contains the caliration coefficients that resulted from the calibration procedure for the countries in which data were available.</p> <p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.