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39 results for “electricity generation”
SignAture_Electricity_generation_data_compare_Latvia_2020_2022
<p>This dataset, related to the article 'Power System Modelling in the Baltic Countries: Data Accessibility and Consistency Aspects' (2023), compares electricity generation data for 2020 and 2022 from various sources in Latvia, providing both input and output values and associated metadata.</p>
PanTaGruEl - a pan-European transmission grid and electricity generation model
<p>If you have any questions or comments, please write to <a href="mailto:laurent.vincent.pagnier@gmail.com">laurent.vincent.pagnier@gmail.com</a>.</p> <p>When publishing results based on this data set, please cite:</p> <p>L. Pagnier, P. Jacquod, “Inertia location and slow network modes determine disturbance propagation in large-scale power grids”, PLOS ONE 14(3): e0213550, 2019. <a href="https://doi.org/10.1371/journal.pone.0213550">PLOS ONE 14(3): e0213550</a>, 2019.</p> <p>and</p> <p>M. Tyloo, L. Pagnier, P. Jacquod, “The Key Player Problem in Complex Oscillator Networks and Electric Power Grids: Resistance Centralities Identify Local Vulnerabilities”, <a href="https://doi.org/10.1126/sciadv.aaw8359">Science Advances 5(11): eaaw8359</a>, 2019.</p> <p><strong>Description:</strong></p> <p>PanTaGruEl is a dynamical grid model designed to investigate the propagation of disturbances in the continental European transmission grid.</p> <p>The construction of the model is detailed <a href="https://doi.org/10.1371/journal.pone.0213550.s002">here</a>.</p> <p><strong>Features</strong>:</p> <ul> <li>Precise distribution of national demands to network buses.</li> <li>Realistic electrical parameters of transmission lines.</li> <li>Merit-Order based economic dispatch of generators.</li> <li>Dynamical parameters of generators and loads for transient stability investigations.</li> </ul> <p><strong>Files:</strong></p> <p>Data files:</p> <p>Our model is provided in an extended Matpower format and as csv raw data. For more information on Matpower format, see Appendix B of its <a href="https://matpower.org/docs/MATPOWER-manual.pdf">manual</a>.</p> <p>Script files:</p> <p><em>opf_ex.m </em>performs optimal power flow computations for two load configurations.<br> <em>spectral_ex.m</em> presents a basic spectral analysis.<br> <em>dynamics</em><em>_ex.m</em> give a minimal example of dynamical simulations.</p> <p><strong>Requirements:</strong></p> <p>Our model has been developed for use with <a href="https://matpower.org/">Matpower</a>. If you are interested in a port to another language, please <a href="mailto:laurent.vincent.pagnier@gmail.com?subject=Info%20on%20PanTaGruEl">contact us</a>.</p> <p><strong>Acknowledgement:</strong></p> <p>The authors thank M. Tyloo and K. Van Walstijn for their useful comments and remarks on the model.</p> <p><strong>Sources</strong>:</p> <p>B. Wiegmans, <a href="https://doi.org/10.5281/zenodo.55853">“GridKit extract of ENTSO-E interactive map”</a><br> Global Energy Observatory, <a href="http://globalenergyobservatory.org">“GEO Power plants database”</a><br> Siemens, <a href="http://siemens.com/power-engineering-guide">“Power Engineering Guide”</a></p>
Task 3 Dataset for Dreaming of Electrical Waves: Generative Modeling of Cardiac Excitation Waves using Diffusion Models
Open the record for dataset details and reuse information.
Weekly plots of Great Britain's half-hourly electrical system weather dependent generation, net imports and overall demand from 2008-11-10
<p>Plots that show the electrical system transition of Great Britain, they were created to form the individual frames for a video of the transition.</p>
Electricity demand data and solar generation data from Plymouth. UK
<p>This dataset was used in the Western Power Distribution Presumed Open Data competition in 2021.</p> <p>The data is provided under the Western Power Distribution Open Data Licence.</p> <p>There are five files:</p> <p>pv_train_set4.csv - contains solar panel data - an irradiance, power output and solar panel temperature for each half-hour from 3rd November 2017 through 2nd July 2020.</p> <p>weather_train_set4.csv - contains hourly reanalysis temperature and solar radiation at 6 weather stations near the solar panels (near Plymouth, UK).</p> <p>demand_train_set4.csv - contains half-hourly electricity demand data from a substation near Plymouth, UK, running from 3rd November 2017 through 2nd July 2020.</p> <p>pv_test_set4.csv - contains an extra week of data to pv_train_set4.csv, running from 3rd July 2020 through 9th July 2020.</p> <p>demand_test_set4.csv - contains an extra week of data to demand_train_set4.csv, running from 3rd July 2020 through 9th July 2020.</p> <p> </p>
University campus buildings electrical and thermal demand and generation.
<p>UVTgv has agreed to share data for both generation and consumption profile of their buildings. The load data is provided as one .csv per building and year with 8760 rows each one representing one hourly consumption of the building. The load datasets comprise 2019 and 2020 load data for electricity and heat demand, for the following buildings: ABR, C, ICSTM. The generation profiles provided are for 3 PV installations (generation profile), one solar thermal plant (capacity factor), and a mini-wind turbine (generation profile).</p> <p> </p>
Fig. 5 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator
Fig. 5 Lateral view of Electrophorus electricus. National Museum of Natural History, NMNH 225670, 520 mm TL. Corantijn River, Suriname
Fig. 4 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator
Fig. 4 Ecological Niche Model and electric organ discharges for species of Electrophorus. Species niche models generated by MaxEnt for Greater Amazonia: a Electrophorus electricus (red); b E. varii (yellow); and c E. voltai (blue). d Measurements of voltage of high-voltage EODs, low-voltage EODs waveforms from Sach's organ, and posterior one-third of Hunter's organ (grey lines = individually recorded fish, black lines = averaged waveform for each species). e Nearest-neighbor hierarchical clustering of prominent time-frequency features of the low-voltage Sach's organ EOD from seven individuals of Electrophorus
Fig. 3 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator
Fig. 3 Electrophorus tree of life and time of species diversification. Time-calibrated genealogy of Electrophorus based on a maximum clade credibility (MCC) species tree derived from *BEAST2.4 analyses of 10 genes (colored lines) and 94 specimens of Electrophorus (relaxed molecular clock and uncorrelated lognormal model implemented). Purple bars represent 95% highest posterior density distributions for the estimated divergence time of each major node. Voltage measurements made by us are reported below E. electricus (National Museum of Natural History, NMNH 225670, 520 mm TL, Corantijn River, Suriname), E. voltai (Museu Paraense Emílio Goeldi, MPEG 15529; holotype, 1290 mm TL), and E. varii (MPEG 25422; holotype, 1000 mm TL)
Fig. 1 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator
Fig. 1 Sampling localities and gene trees for the three species of Electrophorus. a Map of northern South America showing distributions of sampled records and type localities (indicated by numbers) for three electric eel species: Electrophorus electricus (red dots, 1 = Suriname River, Suriname); E. voltai (blue dots, 2 = Rio Ipitinga, Brazil); and E. varii (yellow dots, 3 = Rio Goiapi, Brazil). Bicolor dots (blue/yellow) indicate sympatric co-occurrence of E. voltai and E. varii. The map was created in ArcGIS (https://www.arcgis.com) with images available at Shuttle Radar Topography Mission, Global Multi-resolution Terrain Elevation Data, and HydroSHEDS database. b *BEAST2.4 species tree (top cladogram; 94 specimens: 15 E. electricus, 41 E. voltai, 38 E. varii) based on 5 mitochondrial (trees 1–5; 107 specimens: 19 E. electricus, 43 E. voltai, 45 E. varii) and 5 nuclear genes (6–10; 94 specimens). Higher shading densities represent areas where the majority of trees agree in topology and branch lengths (posterior probabilities>0.99), while lower densities represent areas of uncertainty (Supplementary Data 1)
Fig. 2 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator
Fig. 2 Key morphological features to recognize the three species of Electrophorus. Top, radiographs of lateral view of the anterior portion of body (skull and pectoral girdle highlighted red). The cleithrum lies between the fifth and sixth vertebrae (v) in Electrophorus electricus (a) and E. voltai (b) versus first and second vertebrae in E. varii (c). Bottom, illustrations of ventral view of the head, showing key features listed in Diagnoses. a top: National Museum of Natural History, NMNH 403765, 300 mm TL, Cuyuni River, Guyana; bottom: NMNH 225576, 1000 mm TL, Corantijn River, Suriname. b top: Instituto Nacional de Pesquisas de Amazônia, INPA 39009, 450 mm TL, Teles Pires River, Brazil; bottom: Academy of Natural Sciences of Drexel University, ANSP 197583 (t3539), 1280 mm TL, Xingu River, Brazil. c top: NMNH 306677, 450 mm TL, Lago Janauari, Amazon River, Brazil; bottom: NMNH 196634, 1220 mm TL, Amazon River, Brazil
Fig. 7 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator
Fig. 7 Lateral view of Electrophorus voltai sp. nov. Holotype, Museu Paraense Emílio Goeldi MPEG 15529, 1290 mm TL. Ipitinga River, Brazil
Fig. 6 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator
Fig. 6 Lateral view of Electrophorus varii sp. nov. Holotype, Museu Paraense Emílio Goeldi MPEG 25422, 1000 mm TL. Goiapi River, Brazil
Optimal planning of autonomous electric vehicles charging stations with photovoltaic generations and energy storage systems
<p>This database contains technical information on the 69-bus electrical distribution system. This system was tested in a mixed integer linear programming model for allocating autonomous electric vehicle charging stations equipped with photovoltaic generation and energy storage systems. Additionally, this document contains data related to charging stations, energy storage systems, and operational scenarios applied to the case studies.</p>
PUDL Raw EIA Form 860 -- Annual Electric Generator Report
<p>US Energy Information Administration (EIA) Form 860 data for electric power plants with 1 megawatt or greater combined nameplate capacity. Archived from <a href="https://www.eia.gov/electricity/data/eia860">https://www.eia.gov/electricity/data/eia860</a></p> <p>This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by <a href="https://catalyst.coop">Catalyst Cooperative</a>. It is organized into <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Packages</a>. For additional information about this data and PUDL, see the following resources: </p><ul> <li><a href="https://github.com/catalyst-cooperative/pudl">The PUDL Repository on GitHub</a></li> <li><a href="https://catalystcoop-pudl.readthedocs.io">PUDL Documentation</a></li> <li><a href="https://zenodo.org/communities/catalyst-cooperative/">Other Catalyst Cooperative data archives</a></li> </ul> <p></p>
The amplitudes of electric fields generated by TI stratrgy under different electrode montages
<p>This dataset containes the amplitudes of electric fields generated by extraocular temporally interfering electrical stimulation under all the electrode mongtages mentiond in our research.There also original figures of our research. </p>
Land Use Trade-offs in Decarbonization of Electricity Generation in the American West
<p>This Zenodo archive includes the code and data used to produce the publication Patankar et al. (2022): Land Use Trade-offs in Decarbonization of Electricity Generation in the American West.</p> <p>This analysis requires five types of scripts/modeling efforts that take in three types of inputs, as described below. The code for the GenX model is not included here as it is available at https://github.com/GenXProject/GenX with further documentation available at https://genxproject.github.io/GenX/dev/. </p> <p> **Inputs**<br> 1. Solar and wind candidate project area (CPA)<br> 2. Renewable resource profiles<br> 3. Real and routing cost surface data<br> **Scripts and Models**<br> 1. PowerGenome - Power system data compilation software<br> 2. GenX Power system model along with the Modeling to Generate Alternatives (MGA) algorithm<br> 3. Transmission network building<br> 4. Downscaling for new solar and wind capacity <br> 5. Land use analysis presented in the paper</p> <p>For information on the description of the datasets, scripts and modeling efforts, see the README.md file. </p>
Monophonic Electric Guitar Notes to Train a Timbre Generation VAE
<p>This dataset contains a subset of audio files from the <a href="../doi/10.5281/zenodo.11398029" target="_blank" rel="noopener">SemanticTimbreDataset</a> that were used to train a Timbre Generation VAE.</p> <p>Files are organised as follows: Timbre_Group/Timbre_Descriptor/Timbre_Magnitude.</p> <p>The audio files with a timbre magnitude of 0 in the OscillationFX/Fluttery directory are monophonic electric guitar notes recorded from a Fender Stratocaster originally sourced from the EGFxSet [1]. All other audio files are those original clean samples processed through various guitar pedals affecting the sounds at various intensities (timbre magnitude). Each guitar pedal's effect can be described by a separate timbre descriptor. This training dataset contains 19 timbre descriptor groups in total, where each timbre descriptor group contains audio files with timbre magnitudes of 25, 50, 75, and 100.</p> <p> </p> <p>[1] Hegel Pedroza, Gerardo Meza, & Iran R. Roman. (2022). EGFxSet: Electric guitar tones processed through real effects of distortion, modulation, delay and reverb (Version 1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7044411</p>
VAE-Generated Monophonic Electric Guitar Notes
<p>This dataset contains audio files of monophonic electric guitar notes generated by a Timbre Generation Variational Autoencoder (VAE).</p> <p>The Timbre Generation VAE was prompted to generate E4 electric guitar notes with varying timbral characteristics via each of the 20 timbre descriptors present within the <a href="../doi/10.5281/zenodo.11398029" target="_blank" rel="noopener">SemanticTimbreDataset</a>, leading to the sounds in the `generated' folders contained within the VAE-GeneratedTestSet folder. The original sounds are provided in the `original' folders for comparison to the VAE-generated sounds.</p> <p>Furthermore, 10 5-point linear interpolations were performed between points in the Timbre Generation VAE's latent space, leading to the sounds in the `generated' folders contained within the VAE-InterpolatedTestSet folder. The interpolations' original start and target sounds are provided in the `original' folders for comparison to the VAE-generated sounds. These 10 interpolations were:</p> <ul> <li>Clean E4 Note (Timbre Magnitude 100) to Fuzzy E4 Note (Timbre Magnitude 100)</li> <li>Resonant E4 Note (Timbre Magnitude 100) to Thin E4 Note (Timbre Magnitude 100)</li> <li>Punchy E4 Note (Timbre Magnitude 100) to Soft E4 Note (Timbre Magnitude 100)</li> <li>Shimmery E4 Note (Timbre Magnitude 100) to Jittery E4 Note (Timbre Magnitude 100)</li> <li>Fuzzy E4 Note (Timbre Magnitude 100) to Thin E4 Note (Timbre Magnitude 100)</li> <li>Fuzzy E4 Note (Timbre Magnitude 100) to Soft E4 Note (Timbre Magnitude 100)</li> <li>Fuzzy E4 Note (Timbre Magnitude 100) to Shimmery E4 Note (Timbre Magnitude 100)</li> <li>Thin E4 Note (Timbre Magnitude 100) to Soft E4 Note (Timbre Magnitude 100)</li> <li>Thin E4 Note (Timbre Magnitude 100) to Shimmery E4 Note (Timbre Magnitude 100)</li> <li>Soft E4 Note (Timbre Magnitude 100) to Shimmery E4 Note (Timbre Magnitude 100)</li> </ul>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Cost and performance data for electricity generation and storage technologies
<p>Here, we present a database which collates historical, current, and future cost and performance data and assumptions for the six most prominent electricity generation technologies; coal, gas, hydroelectric, nuclear, solar photovoltaic (PV) and wind power, which together accounted for over 92% of installed generation capacity in 2022. In addition, we provide the same data for utility-scale battery energy storage systems (BESS), regarded as critical to the integration of variable renewables such as wind and solar PV.</p> <p>The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 5510 datapoints from 56 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability in future.</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)
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.