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42 results for “power generation”
Data for Figures 4, A-F and Table J of Publication "Blue skies over China: The effect of pollution-control on solar power generation and revenues"
<p>This repository contains the data to produce Figures 4, A-F and Table J and emission data in the paper:</p> <p>"Labordena M, Neubauer D, Folini D, Patt A, Lilliestam J (2018) Blue skies over China: The effect of pollution-control on solar power generation and revenues. PLoS ONE 13(11): e0207028. https://doi.org/10.1371/journal.pone.0207028"</p> <p>Note that the scripts are to be found in the accompanying package (https://doi.org/10.5281/zenodo.8130726)</p>
Generated Data for the Manuscript "Nonideality-Aware Training for Accurate and Robust Low-Power Memristive Neural Networks"
<p>The file contains data generated and referred to in the text and the figures of the manuscript.</p>
High-power intracavity single-cycle THz pulse generation using thin lithium niobate
<p>This dataset is accompanying the paper "High-power intracavity single-cycle THz pulse generation using thin lithium niobate"<br><br><strong>Autocorrelation.txt:</strong> second harmonic generation noncollinear autocorrelation trace data. (measurement device: Femtochrome FR-103XL)</p><p><strong>Spectrum.txt:</strong> optical spectrum (measurement device: APE wavescan)</p><p><strong>RF_1Mspan.txt:</strong> radio frequency spectrum with 1 MHz span (measurement device: ROHDE & SCHWARZ FPC1000)</p><p><strong>RF_1Gspan.txt:</strong> radio frequency spectrum with 1 GHz span (measurement device: ROHDE & SCHWARZ FPC1000)</p><p><strong>EOS_THz_raw.h5: </strong>electro-optic sampling raw data of the THz measurement in HDF-5 format (measurement device: ROHDE & SCHWARZ RTM3004)</p><p><strong>EOS_noise_raw.h5: </strong>electro-optic sampling raw data of the noise measurement in HDF-5 format (measurement device: ROHDE & SCHWARZ RTM3004)</p><p><strong>THz_time.csv:</strong> processed electro-optic sampling data of the THz measurement in time</p><p><strong>THz_freq.csv:</strong> processed electro-optic sampling data of the THz measurement in frequency</p><p><strong>Dark_time.csv:</strong> processed electro-optic sampling data of the noise measurement in time</p><p><strong>Dark_freq.csv:</strong> processed electro-optic sampling data of the noise measurement in frequency</p>
Open-source quality control routine and multi-year power generation data of 175 PV systems
<p><strong>Description</strong></p> <p>The repository contains an extensive dataset of PV power measurements and a python package (qcpv) for quality controlling PV power measurements. The dataset features four years (2014-2017) of power measurements of 175 rooftop mounted residential PV systems located in Utrecht, the Netherlands. The power measurements have a 1-min resolution.</p> <p><strong>PV power measurements</strong></p> <p>Three different versions of the power measurements are included in three data-subsets in the repository. Unfiltered power measurements are enclosed in <em>unfiltered_pv_power_measurements.csv</em>. Filtered power measurements are included as <em>filtered_pv_power_measurements_sc.csv </em>and<em> filtered_pv_power_measurements_ac.csv</em>. The former dataset contains the quality controlled power measurements after running single system filters only, the latter dataset considers the output after running both single and across system filters. The metadata of the PV systems is added in<em> metadata.csv</em>. This file holds for each PV system a unique ID, start and end time of registered power measurements, estimated DC and AC capacity, tilt and azimuth angle, annual yield and mapped grids of the system location (north, south, west and east boundary).</p> <p><strong>Quality control routine</strong></p> <p>An open-source quality control routine that can be applied to filter erroneous PV power measurements is added to the repository in the form of the Python package qcpv (<em>qcpv.py</em>). Sample code to call and run the functions in the qcpv package is available as <em>example.py.</em></p> <p><strong>Objective</strong></p> <p>By publishing the dataset we provide access to high quality PV power measurements that can be used for research experiments on several topics related to PV power and the integration of PV in the electricity grid.</p> <p>By publishing the qcpv package we strive to set a next step into developing a standardized routine for quality control of PV power measurements. We hope to stimulate others to adopt and improve the routine of quality control and work towards a widely adopted standardized routine. </p> <p><strong>Data usage</strong></p> <p>If you use the data and/or python package in a published work please cite: <em>Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2022. Open-source quality control routine and multi-year power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy.</em></p> <p><strong>Units</strong></p> <p>Timestamps are in UTC (YYYY-MM-DD HH:MM:SS+00:00).</p> <p>Power measurements are in Watt.</p> <p>Installed capacities (DC and AC) are in Watt-peak.</p> <p><em><strong>Additional information</strong></em></p> <p>A detailed discussion of the data and qcpv package is presented in: <em>Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2022. Open-source quality control routine and multi-year power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy. Corrections are discussed in: Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2024. </em><em>Erratum: Open-source quality control routine and multiyear power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy.</em></p> <p><strong>Acknowledgements </strong></p> <p>This work is part of the Energy Intranets (NEAT: ESI-BiDa 647.003.002) project, which is funded by the Dutch Research Council NWO in the framework of the Energy Systems Integration & Big Data programme. The authors would especially like to thank the PV owners who volunteered to take part in the measurement campaign. </p>
"Demand-side policies for power generation in response to the energy crisis: A model analysis for Italy", scripts and data
<p>This repository contains the data, scripts and results for the paper "Demand-side policies for power generation in response to the energy crisis: A model analysis for Italy", https://doi.org/10.1016/j.esr.2024.101329.</p> <p>Results in the paper are divided into three sections, corresponding to the numbers of the folders inside this dataset. They are described as follows:</p> <p>1 - EU policy impact: What is the impact on the Italian electricity of the european proposal of cutting power demand and shifting it during peak hours on gas consumption, system costs and emissions?</p> <p>2 - Gas cost sensitivity: Which would be Italy’s most convenient power system considering different gas prices?</p> <p>3 - DSM in mitigation: What could be the role of demand side measures in power systems with a high penetration of RES?</p>
Bias correction of simulated Brazilian wind power generation based on reanalysis data
<p>Available data:</p> <p>- Brazilian wind power generation time series derived from MERRA-2 reanalysis data with wind speed and wind power bias correction.</p> <p>- Wind speed correction factors derived from INMET wind speeds (http://www.inmet.gov.br/portal/) as well as wind power correction factors dervied from ONS wind power generation time series are also provided.</p> <p>- Simulation of about 38 years of wind power generation with fixed capacity.</p> <p>Data used for validation:</p> <p>- Historical wind power generation data, which were used for validation of simulated time series, can be found at the ONS homepage (http://ons.org.br/Paginas/resultados-da-operacao/historico-da-operacao/geracao_energia.aspx).</p> <p> </p> <p>Other Links:</p> <p>- Information on this will soon be found here: https://refuel.world/</p> <p>- Code for generating time series, validation and analysis: https://github.com/KatharinaGruber/BrazilWind</p> <p>- Master thesis belonging to data: https://doi.org/10.5281/zenodo.1471221</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>
Dataset for "High power single crystal KTA optical parametric amplifier for efficient 1.4–3.5 µm mid-IR radiation generation"
<p>The dataset represents the experimental data for publication "High power single crystal KTA optical parametric amplifier for efficient 1.4–3.5 µm mid-IR radiation generation".</p>
Measured weather variables and power generation from vertical agrivoltaic installation in Foulum, Denmark
<p>Measured weather variables and power generation from vertical agrivoltaic installation in Foulum, Denmark.</p> <p>Data is recorded every 5 minutes for the period December 2022 to October 2024. See the figure 'summary_clean_data.jpg' for an overview of data availability.</p> <p>Data is collected and curated in the following Github repository: https://github.com/martavp/agrivoltaic_foulum</p> <p>The Agrivoltaic demonstration system is described in the pre-print <a href="https://www.researchsquare.com/article/rs-5358908/v1" rel="nofollow">"Vertical Agrivoltaics in a Temperate Climate: Exploring Technical, Agricultural, Meteorological, and Social Dimensions"</a></p>
Nuclear Power Generation Phaseouts Redistribute U.S. Air Quality and Climate Related Mortality Risk, Data
<p>This dataset accompanies the publication, "Nuclear Power Generation Phaseouts Redistribute U.S. Air Quality and Climate Related Mortality Risk", and can be used with the code located at https://zenodo.org/badge/latestdoi/248010532 to reproduce our results.</p>
Dataset on PowerWorld Software Power Flow Calculations on an Underground Distribution Feeder for Inserting Renewable Distribution Generation from Biogas, Photovoltaic and Small Wind Sources
<p>This Dataset brings all the information, details and source files used for power flow studies of the USP-105 underground feeder of the distribution medium voltage network in the University of São Paulo campus, which has received several embedded DG sources, namely a biogas plant, photovoltaic units and a small wind turbine.</p> <p>The power flow simulations were realized using the PowerWorldTM Simulator, v.23</p> <p>The files types on the Dataset are: </p> <p>.pwb, .pwd and tsb: Powerworld software input files for the simulations</p> <p>.csv: where a semicolon symbol (;) is used as a column separator, while a dot symbol (.) represents the decimal separator. The first row of each CSV file corresponds to the header row to help identify data.</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>
High Average Power Second-harmonic Generation of a CW Erbium Fiber MOPA
<p>Open access data set for the manuscript "High Average Power Second-harmonic Generation of a CW Erbium Fiber MOPA" to be published in Photonics Technology Letters.</p>
Life-cycle greenhouse gas emissions in power generation using palm kernel shell
<p>Although the Japanese feed-in tariff was introduced to expand renewable energy, leading to the expansion of palm kernel shell (PKS) use, the greenhouse gas (GHG) emission reduction effect is evaluated using the limited life-cycle of PKS, focusing on processes after PKS generation point. Therefore, this study aimed to elucidate the life-cycle GHG emissions of power generation using PKS. We targeted two PKS-firing power plants as these are the first two instances of the use of PKS in power plants in Japan. A system boundary was established to cover palm plantation management in Indonesia and Malaysia, as both power plants import PKS from these countries. The GHG emissions were derived from land-use change, palm plantation, oil extraction, PKS transportation, and power plants. Six scenarios were examined for the emissions based on the type of land-use change and the existence of biogas capture in oil extraction. CO<sub>2</sub> emissions from PKS combustion were also calculated by assuming that carbon neutrality was lost because of cultivation abandonment. The GHG emissions in one scenario, where the plantations were replanted and continuously managed and no biogas capture implemented in oil extraction, exhibited an average of 0.134 kg-CO<sub>2</sub>eq/kWh reduction in a plant in Kyushu District, and 0.043 kg-CO<sub>2</sub>eq/kWh reduction in a plant in Shikoku District for liquid natural gas-fired steam power generation, respectively. More than 65% of life-cycle GHG emissions originate from biogas generated during oil extraction; thus, biogas capture is an effective strategy to reduce current emissions. In contrast, in the case of accompanying land-use change or collapse of carbon neutrality, the emissions considerably exceeded those of fossil fuels. These findings indicated that the FIT fails to consider the risk of increased emissions or further substantial emission reductions. Therefore, the feasibility of FIT application to PKS needs to be re-established by evaluating the entire PKS life-cycle. </p>
Sustainable power generation for at least one month from ambient humidity using unique nanofluidic diode
<p>The continuous energy-harvesting in moisture environment is attractive for the development of clean energy source. Controlling the transport of ionized mobile charge in intelligent nanoporous membrane systems is a promising strategy to develop the moisture-enabled electric generator. However, existing designs still suffer from low output power density. Moreover, these devices can only produce short-term (mostly a few seconds or a few hours, rarely for a few days) voltage and current output in the ambient environment. Here, we show an ionic diode–type hybrid membrane capable of continuously generating energy in the ambient environment. The built-in electric field of the nanofluidic diode-type PN junction helps the selective ions separation and the steady-state one-way ion charge transfer. This directional ion migration is further converted to electron transportation at the surface of electrodes via oxidation-reduction reaction and charge adsorption, thus resulting in a continuous voltage and current with high energy conversion efficiency.</p>
Pre-generated network files for "Intersecting near-optimal spaces: European power systems with more resilience to weather variability"
<p>These are network files that can be used to investigate the impacts of weather variability on the European power system using PyPSA-Eur as in <a href="https://github.com/aleks-g/intersecting-near-opt-spaces/tree/v1.0">https://github.com/aleks-g/intersecting-near-opt-spaces/tree/v1.0</a>. Find more information about the approach in the README of that repository.</p> <p>These network files are a shortcut to reproduce the results and use a fixed configuration ("v1.0"). For other configurations, it may be necessary to download ERA5 reanalysis cutouts (more on this in the git repository).</p> <p>Instructions can be found in the git repository.</p>
Dataset for Next-Generation Self-Powered Photodetectors using 2D Bismuth Oxide Selenide Crystals
<p>The dataset contains relevant data and figures regarding the manuscript "Next-Generation Self-Powered Photodetectors using 2D Bismuth Oxide Selenide Crystals".</p> <p>All Figures are in jpg/tiff format and all relevant data are in csv formats. </p> <p>The data in csv format are labelled as specified in the corresping images (e.g. Figure 1a csv file corresponds to data used to plot graphs from Figure 1a etc.). </p> <p>Axis labeling and units are always specified at the beginning of individual columns. If more than one curve was plotted from the csv file, the conditions can also be found at the beginning of corresponding columns.</p>
EMHIRES dataset: wind and solar power generation
<p><strong>EMHIRES Wind</strong></p> <p>The first version of EMHIRES dataset releases four different files about the wind power generation hourly time series during 30 years (1986-2015), taking into account the existing wind fleet at the end of 2015, for each country (onshore and offshore), bidding zone and by NUTS 1 and NUTS 2 region. The time series are given as capacity factors. The installed capacity used accounted for calculating the capacity factors are summarised in the annexes of the report.</p> <p>https://setis.ec.europa.eu/emhires-dataset-part-i-wind-power-generation_en</p> <p><strong>EMHIRES Solar</strong></p> <p>EMHIRES provides RES-E generation time series for the EU-28 and neighbouring countries. The solar power time series are released at hourly granularity and at different aggregation levels: by country, power market bidding zone, and by the European Nomenclature of territorial units for statistics (NUTS) defined by EUROSTAT; in particular, by NUTS 1 and NUTS 2 level. The time series provided by bidding zones include special aggregations to reflect the power market reality where this deviates from political or territorial boundaries.</p> <p>The overall scope of EMHIRES is to allow users to assess the impact of meteorological and climate variability on the generation of solar power in Europe and not to mime the actual evolution of solar power production in the latest decades. For this reason, the hourly solar power generation time series are released for meteorological conditions of the years 1986-2015 (30 years) without considering any changes in the solar installed capacity. Thus, the installed capacity considered is fixed as the one installed at the end of 2015. For this reason, data from EMHIRES should not be compared with actual power generation data other than referring to the reference year 2015.</p> <p>https://setis.ec.europa.eu/emhires-dataset-part-ii-solar-power-generation_en</p>
Life-cycle greenhouse gas emissions in power generation using palm kernel shell
Open the record for dataset details and reuse information.
Supplementary Material for "How do seasonal and technical factors affect generation efficiency of photovoltaic power plants?"
<p>Supplementary material for the paper "How do seasonal and technical factors affect generation efficiency of photovoltaic power plants?".</p><p>This supplementary information provides:</p><ul><li>Table S1. Monthly solar irradiation for each plants</li><li>Table S2. Summary of input and output factors, and efficiency scores</li><li>Table S3. Rainy season of the northern Kyushu region</li><li>Table S4. Summary of parameters for the regression model</li><li>Table S5. Average monthly solar irradiation for three cities</li><li>Figure S1. Regression line for each of the PV power plants</li></ul>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.