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96 results for “Photovoltaic”
Rooftop photovoltaic (PV) potential data for the Swiss building stock
<p>The provided dataset contains data for the PV potentials on building rooftops, evaluated for 9.6 M roof surfaces in Switzerland in an hourly temporal resolution. The methodology of the generation of the dataset is described in:</p> <p>Walch, Alina, Roberto Castello, Nahid Mohajeri, and Jean-Louis Scartezzini. “Big Data Mining for the Estimation of Hourly Rooftop Photovoltaic Potential and Its Uncertainty.” <em>Applied Energy</em> 262 (March 15, 2020): 114404.</p> <p>In the process of generating this dataset, the following aspects were included:</p> <ul> <li>Meteorological conditions in Switzerland (solar radiation, temperature, snow cover)</li> <li>Local shading and sky coverage from surrounding buildings and trees (based on a Digital Surface Model)</li> <li>Obstruction of roof surface due to roof superstructures such as dormers and chimneys (estimated based on data from the canton of Geneva)</li> <li>The panel and inverter efficiencies, as a function of the solar radiation and temperature</li> </ul> <p>Several aspects were estimated and hence include some uncertainty, due to the input datasets and the modelling methodology. For details on the sources of uncertainty and the limitations, please refer to the referenced article. Estimates for these uncertainties are provided alongside the variables. A description of the metadata is provided in the document <em>rooftop_PV_CH_metadata_V1.pdf.</em></p> <p><strong>Data description:</strong></p> <p>The rooftop PV potential data has been computed at monthly-mean-hourly temporal resolution (i.e. 24 hours for each of the 12 months) for each individual roof surface, based on a national roof surface dataset created by SwissTopo (see https://www.uvek-gis.admin.ch/BFE/sonnendach/). The data given in this dataset is aggregated, in order to make the data easier to use for studies inside as well as outside Switzerland, to reduce the file size and to respect license agreements. Two types of aggregation are provided:</p> <ol> <li>Aggregation per building, using the object ID of the SwissBuildings3D cadastre as identifier. </li> <li>Aggregation per roof type, separating between 4 categories: Tilt angle, aspect angle, roof area, altitude</li> </ol> <p>If a different type of aggregation or the data per individual roof surface is required, please do not hesitate to get in touch with the authors directly.</p>
Dataset for Accuracy of Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and CNN-LSTM Model
<p>This research paper introduces a deep learning hybrid model employing Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting.The proposed method integrates the Variational Mode Decomposition (VMD) algo-rithm with the CNN-LSTM model to predict PV power generation from a solar farm in Boussada, Algeria, from January 1, 2019, to December 31, 2020. The performance of the developed model is benchmarked against other deep learning models (VMD-CNN, VMD-LSTM, CNN-LSTM) across various time horizons (15, 30, and 60 minutes) to provide a comprehensive evaluation. Our findings exhibit greater performance of the developed model compared to other architectures, showcasing promising results in solar power forecasting. This research contributes to the main goal of enhancing EMS by providing accurate solar energy forecasts.</p>
Wave basin tests of multi-body floating photovoltaics system and an external floating breakwater.(SUREWAVE project)
<p><span>The aim of the EU Horizon Europe project SUREWAVE (2022-2025) is to develop a floating PV solution for offshore environments. A concrete floating breakwater (FBW) configuration will be designed to provide shelter for the floating PV (FPV) against harsh environmental conditions. MARIN’s scope is to support the hydrodynamic design of the system through numerical simulations and wave basin tests. Basin tests are scheduled at two stages of the project: (1) at early design stage (for a global understanding of the preliminary design); (2) at final design stage (for verification and demonstration). The present dataset contains the reuslts of the early stage design stage wave basin testing.</span></p>
Global maps of area suitability for solar photovoltaics on rooftops and cropland
<p>This dataset consists of the following global maps:</p> <p>1. The suitable area for installing agri-photovoltaics or agrivoltaics on agricultural land cover in about 10 km x 10 km resolution for three scenarios of policy and social acceptance (optimistic, neutral and conservative)</p> <p>2. The percentage of PV-suitable building area of total area in 10km x 10km resolution</p> <p>3. The area suitability of rooftop-photovoltaics in a 10km x 10km resolution </p> <p>The maps are based on the land cover data from the Copernicus Land Monitoring Service from 2019 and the Open Street Map accessed in 2021.</p> <p>Updates from Version 1.0 :</p> <p>- New maps of agrivoltaic suitability due to updated methodology : inclusion of regional crop distribution </p>
Dataset for "Machine Learning Stability and Bandgaps of Lead-Free Perovskites for Photovoltaics"
<p>Datasets used in the publication "Machine Learning Stability and Bandgaps of Lead-Free Perovskites for Photovoltaics" [doi:10.1002/adts.201900178].</p> <p>All structures were relaxed with the following parameters using Quantumwise QATK 2017:</p> <p>- SG15-GGA norm-conserving (Vanderbilt) pseudopotentials employed in a LCAO-approach (200 Hartree cutoff)<br> - 2x1x2-cubic-perovskite-supercells, relaxed from cubic 11.4Åx5.7Åx11.4Å-structures (forces < 0.01eV/Å)<br> - 300K Fermi-Dirac-smearing<br> - a 6x12x6 k-point grid (Monkhorst-Pack)</p> <p><br> Specifically, the included files are:</p> <p><strong>db_2.data: </strong>the actual database used for model building (json-format)<br> <strong>lead_set.data:</strong> the "external" test set used to test predictive power with out of sample compounds (json-format)<br> <strong>load_stanley_c.py:</strong> a python script to parse the .json-files to a python-dictionary including the structures (relaxed and unrelaxed) as <a href="https://gitlab.com/ase/ase">ASE</a>-atoms</p> <p>The format of the datafiles is as follows (-1 generally denote values not parsed from the raw data):<br> {<br> "<idstring>" : {<br> "trajectory" : n/a,<br> "energy" : total DFT energy in eV,<br> "rstruc" : relaxed structure, 3-tuple: (cell-vectors, scaled_positions, elements),<br> "gaps" : { "opt_gap", "ind_gap } - both direct and indirect gap,<br> "effective_mass" : n/a,<br> "iterations" : number of relaxation steps,<br> "calc" : some calculation metadata,<br> "ustruc" : unrelaxed input structure,<br> <br> }<br> }<br> Missing ids relate to structures filtered out, because the calculation didn't converge.</p> <p>Some code which works with a different representation of this data can be found at https://github.com/jstanai/Machine-Learning-Perovskite-Properties-for-Photovoltaics</p> <p> </p> <p> </p>
Time series of electricity output for large grid connected photovoltaic installations in Chile
<p>These data sets accompany the paper "Simulation of multi-annual time series of solar photovoltaic power: is the ERA5-land reanalysis the next big step?". They include capacity factors (values 0 to 1) in hourly temporal resolution of 103 large PV installations in Chile derived from official sources as well as simulated capacity factors using PV_LIB with ERA5-land and MERRA-2 reanalysis data. The data covers the period 2014-2018 and simulations were performed assuming either a "fixed" system with orientation towards north and inclination equal to the latitude (i.e. optimal inclination) or a horizontal single axis “tracking” system with backtracking. Furthermore, accuracy indicators (Pearson’s correlation, mean bias error and root mean square error) are provided, comparing the simulated time series with capacity factors derived from official sources. Capacity factors were calculated for the 103 installations and both alternative configurations (“fixed” and “tracking”) but only a subset of 23 installations has reference data of sufficient quality to allow for a validation (for a more detailed description, see the paper). Indicators were also calculated for all installations and configurations but should be only compared for installations inside a particular configuration: there are 14 systems classified as “tracking” and 9 as “fixed”. Further details are available in the paper and the entire Python and R code is available on github at <a href="https://github.com/inwe-boku/PV_from_era5">https://github.com/inwe-boku/PV_from_era5</a>. </p>
Numerical simulations of AZO/ZnGeO/Cu2O solar cells: Impact of the germanium composition of the buffer layer and the use of low cost fabrication on the photovoltaic performances
<p>The dataset contains the results of the numerical simulations of AZO/ZnGeO/Cu2O solar cell models.</p> <p>The physical parameters of the model are chosen with special care to match literature experimental measurements or are interpolated using the values from binary metal oxides in the case of the new ZnGeO compound. The solar cell structure includes an interface and a defective layer at the ZnGeO/Cu2O heterojunction.</p> <p>The AZO/ZnGeO/Cu2O model results reproduce the photovoltaic characteristics of experimental devices presented by Minami et al. (Applied Physics Express 9, 052301 (2016) DOI:10.7567/APEX.9.052301)</p> <p>The dataset also includes results using models with different germanium compositions for the ZnGeO buffer layer.</p> <p>Other solar cell simulation results are presented to model the impact of low cost fabrication processes, such as spray pyrolysis, by varying the thickness, doping concentration, carrier mobilities and defect concentration of the Cu2O absorber layer as well as the germanium composition of the buffer layer.</p>
Probabilistic projections of granular energy technology diffusion at subnational level - solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland
<p>The probabilistic projections are part of the work: <br><em>Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, Volume 2, Issue 10, October 2023, pgad321, </em><a href="https://doi.org/10.1093/pnasnexus/pgad321"><em>https://doi.org/10.1093/pnasnexus/pgad321</em></a></p> <p>Please cite the article together with the Zenodo link when you use the data.</p> <p>The provided data files contain the estimated probabilistic projections for all Swiss municipalities on the actual diffusion of solar photovoltaics (PV), heat pumps, and battery electric vehicles (BEVs) in Switzerland for the indicated years:</p> <p>Version 2022-2050: Projections for the years 2022-2050 as presented by Zielonka et. al (2023), PNAS Nexus.<br>Version 2023-2050: Projections for the years 2023-2050, using the latest data of 2022.<br>Version 2024-2050: Projections for the years 2024-2050, using the latest data of 2023.</p> <p>The computations were performed at University of Geneva using Baobab HPC service.</p> <p>This research was carried out with the support of the Swiss Federal Office of Energy SFOE as part of the SWEET project SURE (N.Z., E.T.) and the Swiss National Science Foundation Eccellenza Grant as part of the project "Accuracy of long-range national energy projections" (Grant no. 186834, X.W., E.T.). The authors bear sole responsibility for the conclusions and the results.</p>
Dataset for "A 21 m Operation Range RFID Tag for "Pick to Light" Applications with a Photovoltaic Harvester"
<p>In the paper, a novel Radio-Frequency Identification (RFID) tag for “pick to light” applications is presented. The proposed tag architecture shows the implementation of a novel voltage limiter and a supply voltage (VDD) monitoring circuit to guarantee a correct operation between the tag and the reader for the “pick to light” application. The feasibility to power the tag with different photovoltaic cells is also analyzed, showing the influence of the illuminance level (lx), type of source light (fluorescent, LED or halogen) and type of photovoltaic cell (photodiode or solar cell) on the amount of harvested energy. Measurements show that the photodiodes present a power per unit package area for low illuminance levels (500 lx) of around 0.08 μW/mm<sup>2</sup>, which is slightly higher than the measured one for a solar cell of 0.06 μW/mm<sup>2</sup>. However, solar cells present a more compact design for the same absolute harvested power due to the large number of required photodiodes in parallel. Finally, an RFID tag prototype for “pick to light” applications is implemented, showing an operation range of 3.7 m in fully passive mode. This operation range can be significantly increased to 21 m when the tag is powered by a solar cell with an illuminance level as low as 100 lx and a halogen bulb as source light.</p> <p>This dataset contains some of the data gathered during the experimental work developed and used in the paper.</p>
The dataset of photovoltaic power plant distribution in China by 2020
<p>Photovoltaic (PV) technology, an efficient solution for mitigating the impacts of climate change, has been increasingly used across the world to replace fossil-fuel power to minimize greenhouse gas emissions. With the world's highest cumulative and fastest built PV capacity, China needs to assess the environmental and social impacts of these established photovoltaic (PV) power plants. However, a comprehensive map regarding the PV power plants' locations and extent remain scarce on the country scale. This study developed a workflow combining machine learning and visual interpretation methods with big satellite data to map PV power plants across China. We applied a pixel-based Random Forest (RF) model to classify the PV power plants from composite images in 2020 with 30-meter spatial resolution on Google Earth Engine (GEE). The result classification map was further improved by a visual interpretation approach. Eventually, we established a map of PV power plants in China by 2020, covering a total area of 2917 km<sup>2</sup>. We found that most PV power plants were sited on cropland, followed by barren land and grassland based on the derived national PV map. In addition, the installation of PV power plants has generally decreased the vegetation cover. This new dataset is expected to be conducive to policy management, environmental assessment, and further classification of PV power plants.</p>
Acceleration Research on Novel Photovoltaic Materials
<p>Data and Simulation definiton file for SCAPS1D simulation that are the basis for figures 3-5 of publication DOI:10.1039/d2fd00085g, published in Faraday Discussions (2022)</p> <p>Device structure for the drift-diffusion simulation is: </p> <p>metal back-contact/p-type absorber(1 micron)/n-type buffer layer (30nm)/i-ZnO(80nm)/n-type ZnO(100nm)</p> <p>No interface recombination and no back contact recombination is assumed.</p>
Photovoltaic time series for European countries and different system configurations
<p>This repository comprises 38 years-long hourly time series representing the photovoltaic (PV) capacity factors in every European country (EU-28 plus Serbia, Bosnia-Herzegovina, Norway, and Switzerland). The term capacity factor is defined as the ratio between the delivered power and the cumulative installed capacity (DC). 3 letter codes (ISO-3166-3) are used to identify the countries. Time series include years from 1979 to 2017.</p> <p>To obtain PV time series irradiance from Climate Forecast System Reanalysis (CFSR) dataset has been converted into electricity generation and aggregated at country level. The PV model used for the conversion is described in the article linked below. Prior to conversion, reanalysis irradiance is bias corrected using satellite-based SARAH dataset and a globally-applicable methodology, which is also described in the article.</p> <p>For every country, four different time series assuming alternative PV configurations, <em>i.e</em>., rooftop, optimum tilt, 2-axis tracking, and delta are provided. To obtain the PV hourly capacity factors for a country, different assumptions on the shares of the alternative configurations can be made and the weighted time series can be aggregated accordingly.</p> <p>The license for the AU REatlas photovoltaic time series dataset is: <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International (CC BY 4.0)</a></p> <p>When using this data please make sure you include the following citation:</p> <p><em>M. Victoria and Gorm B. Andresen, Using validated reanalysis data to investigate the impact of the PV system configurations at high penetration levels in European countries, Progress in Photovoltaics: Research and Applications (2019) </em><a href="https://doi.org/10.1002/pip.3126">https://doi.org/10.1002/pip.3126</a></p> <p>More information can be requested from M. Victoria (<a href="mailto:mvp@eng.au.dk">mvp@eng.au.dk</a>) and Gorm B. Andresen (<a href="mailto:gba@eng.au.dk">gba@eng.au.dk</a>).</p> <p>Version 2 assumes tilt angle of 60º for PV panels in delta configuration (in version 1, tilt angle in delta configuration is equal to latitude). The remaining files do not change.</p> <p>Version 3 includes one additional file corresponding to country-wise time series obtained assuming 1 axis-tracker (horizontal axis oriented North-South). In addition, small corrections of the previous time series have been implemented affecting only early hours in the day.<br> </p>
A global inventory of solar photovoltaic generating units - dataset
<p>This is the data repository accompanying Kruitwagen, L., Story, K., Friedrich, J., Byers, L., Skillman, S., & Hepburn, C. (2021) A global inventory of photovoltaic solar generating units, <strong>Nature, </strong><em>forthcoming</em>. This repository contains the training, cross-validation, test, and predicted data set as described in the publication. The contents of this repository are briefly summarised here, see the publication for further details.</p> <p><strong>Repository contents:</strong></p> <p><em>trn_tiles.geojson: </em>18,570 rectangular areas-of-interest used for sampling training patch data.</p> <p><em>trn_polygons.geojson: </em>36,882 polygons obtained from OSM in 2017 used to label training patches.</p> <p><em>cv_tiles.geojson: </em>560 rectangular areas-of-interest used for sampling cross-validation data seeded from <a href="https://www.wri.org/research/global-database-power-plants">WRI GPPDB</a></p> <p><em>cv_polygons.geojson: </em>6,281 polygons corresponding to all PV solar generating units present in cv_tiles.geojson at the end of 2018.</p> <p><em>test_tiles.geojson: </em>122 rectangular regions-of-interest used for building the test set.</p> <p><em>test_polygons.geojson: </em>7,263 polygons corresponding to all utility-scale (>10kW) solar generating units present in test_tiles.geojson at the end of 2018.</p> <p><em>predicted_polygons.geojson: </em>68,661 polygons corresponding to predicted polygons in global deployment, capturing the status of deployed photovoltaic solar energy generating capacity at the end of 2018.</p>
Meteorological data from the experimental period of the submersion test of photovoltaic cables
<p>Meteorological data recorded by the onsite weather station (coordinates: 38°31'50.0"N 8°00'40.3"W) regarding the study of submersion of photovoltaic cables (with two different insulation materials) in freshwater and artificial seawater. The metereological data is logged with 1minute time resolution for the period from 16/10/2020 to 25/01/2021.</p> <p>The meteorological station is composed by:</p> <p>Kipp and Zonen Solys2 Sun tracker</p> <p>Kipp and Zonen CMP6 Pyranometer (horizontal global solar radiation, data units W/m2)</p> <p>RH and Air temperature sensor (air relative humidity, data units % and ambient air temperature, data units ºC)</p> <p>Rain Gauge (precipitation, data units mm)</p>
In Situ Photoluminescence Imaging Dataset of Blade-Coated Perovskite Photovoltaics
<p><strong>Content:</strong></p> <p>The dataset contains time-resolved in situ images acquired during the formation of the perovskite layer which is then built into a perovskite solar cell. The image time series in the dataset encompass the drying and crystallization of the blade-coated perovskite thin-films. An initial exploration of the data presented in the dataset is conducted in the paper <strong><a href="https://doi.org/10.1002/solr.202201114">Process Insights into Perovskite Thin-Film Photovoltaics from Machine Learning with In Situ Luminescence Data</a>.</strong></p> <p>A total of 1,129 solar cells were fabricated using the blade coating deposition method. To monitor the vacuum quenching process of the perovskite layer, a photoluminescence (PL) imaging setup was used to capture four channels of image data. These channels included time series images (2D+t) captured through various spectral filters, with one channel showing reflectance and the other three showing different parts of the PL spectrum. The three PL channels with different spectral transmissions were also used to compute a image time series of spatially resolved PL peak wavelengths. All images were cropped into smaller patches of 65x56 pixels each, which only included the active area of a single solar cell.</p> <p>Different metrics are available as target variables. For each solar cell in the dataset, the photovoltaic performance parameters, namely (1) power conversion efficiency (PCE), (2) open-circuit voltage (<em>V<sub>OC</sub></em>), (3) short-circuit current density (<em>J<sub>SC</sub></em>), and (4) fill factor (FF)), are available (measured backward and forward, as well as the average between forward and backward). Furthermore, information about the perovskite layer thickness of each solar cell’s active area is provided: mean thickness, root-mean-square thickness, and peak-2-valley thickness. Also, additional information like substrate ID and the position of each solar cell within its substrate is provided.</p> <p>All solar cells were fabricated using the same materials, methods, and experimental parameters. As a result, the dataset can be used to apply machine learning techniques to identify variations in the fabrication process between iterations, improve understanding of the process, and predict performance in-line before completing the half-stack into a functional solar cell.</p> <p>Further information on the experimental acquisition procedure can be found in the paper <a href="https://doi.org/10.1002/solr.202201114"><strong>Process Insights into Perovskite Thin-Film Photovoltaics from Machine Learning with In Situ Luminescence Data</strong>.</a></p> <p> </p> <p><strong>Usage:</strong></p> <p>The dataset is made available as a single hdf5-file. The npy-data can be extracted using the notebook “00_extract_data_from_hdf5_file.ipynb” which is provided in the GitHub repository <a href="https://github.com/AI-InSu-Pero/ML-PerovskitePV-InSituLuminescene">https://github.com/AI-InSu-Pero/ML-PerovskitePV-InSituLuminescene</a> </p> <p>The structure of the dataset after extraction from the hdf5-file is depicted below. The dataset (1,129 solar cells) is split into two subfolders, containing train (780 solar cells) and test data (349 solar cell), respectively. For training and test data, the corresponding labels are listed in csv files. In the train and test folders, there are subfolders for each of the substrate assigned to either of the two sets. In the substrate folders, the data for all the patches of a substrate is saved in npy-format with the shape (719, 5, 65, 56), representing (time step, channel, image height, image width). It can be loaded using numpy.load(path_to_file). The order of the five channels is as follows: (0) reflectance, (1) entire PL spectrum, (2) filtered PL spectrum – longer wavelengths remaining, (3) filtered PL spectrum – shorter wavelengths remaining, (4) computed peak wavelength of PL spectrum.</p> <p>In the train folder, an additional folder “cv_splits_5fold” gives the train and validation splits for the 5-fold cross-validation used in the dataset exploration paper. For each fold, the labels are given as csv-files for train and validation split.</p> <p> </p> <pre><code>dataset ├── train │ ├── ACA │ │ ├── 11.npy │ │ ├── 12.npy │ │ ├── 13.npy │ │ ├── 14.npy │ │ ├── 21.npy │ │ └── ... (all other patches of this substrate) │ ├── ACA │ │ ├── 11.npy │ │ ├── 12.npy │ │ ├── 13.npy │ │ ├── 14.npy │ │ ├── 21.npy │ │ └── ... (all other patches of this substrate) │ ├── ... (all other train substrates) │ ├── cv_splits_5fold │ │ ├── fold0 │ │ │ ├── train.csv │ │ │ └── val.csv │ │ └── ... (all other folds) │ └─── labels.csv └── test ├── ACE │ ├── 11.npy │ ├── 12.npy │ ├── 13.npy │ ├── 14.npy │ ├── 21.npy │ └── ... (all other patches of this substrate) ├── ... (all other test substrates) └── labels.csv </code></pre> <p> </p>
Experimental data of photovoltaic cable submersion tests
<p>Experimental data tables regarding the study of submersion of photovoltaic cables (with two different insulation materials) in freshwater and artificial seawater. Subjected to real life conditions, replicating when FPV systems are located in reservoirs or in the marine environment. Electrical insulation tests were carried out weekly to assess possible cable degradation, the physical-chemical characteristics of the water were also periodically monitored, complemented by analysis to detect traces of copper and microplastics in the water.</p>
All-inorganic micrometric CsPbBr3:Yb3+ powder as a multifunctional material for photovoltaics and optical thermometry: structural and optical characterization
<p>Halide perovskites have been studied very intensively by researchers during the last decade. Development of these materials has improved their unique optoelectrical properties reaching even higher standards making them promising candidates for photovoltaic applications. It should be noted that most inorganic halide perovskites obtained to date are synthesized using organic solvents in the form of nanosized colloids. Here, a low-temperature synthesis protocol for the preparation of microcrystalline CsPbBr<sub>3</sub> perovskite powder doped with Yb<sup>3+</sup> ions is proposed. The structural and photoluminescence features of the studied material have been thoroughly investigated and described. It turned out that the excitation of the CsPbBr<sub>3</sub>:Yb<sup>3+</sup> perovskite with a 375 nm wavelength leads to spontaneous luminescence of excitons and Yb<sup>3+ </sup>ions. Hence, the use of CsPbBr<sub>3</sub>:Yb<sup>3+</sup> as a luminescent thermometer or an additional absorbing layer on a solar cell surface is possible. The latter application may result in an increase in the conversion efficiency of the cell. In order to verify this, such a layer was prepared and installed on a commercial silicon solar cell. Its photovoltaic properties have been investigated by the measurements of current-voltage characteristics with 1-sun illumination and spectral characteristics of external quantum efficiency.</p>
Photovoltaic heat pump experiemental data
<p>A prototype of a photovoltaic heat pump (PV-HP) system has been implemented and characterized for cooling generation. The raw data obtained for two different control algorithms are presented here. </p>
Photovoltaic generation and temperature for the year 2019
<p>This dataset has photovoltaic generation data and temperature data regarding a research building in ISEP/P.Porto (Instituto Superior de Engenharia do Porto / Politécnico do Porto). The data was measured using 5-minutes periods during the entire year of 2019. The temperature sensor was located near the photovoltaic panels (without having direct sunlight). The photovoltaic installation has a theoretical peak generation of 7.5 kW.<br>The dataset presents some errors in the data, representing failures in the acquisition system.</p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
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>
ScienceDex guides
Understand access before you commit
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.