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96 results for “Photovoltaic”
Dataset for detecting the electrical behavior of photovoltaic panels from RGB images
<p>The dynamic reconfiguration and maximum power point tracking in large-scale photovoltaic (PV) systems require a large number of voltage and current sensors. In particular, the reconfiguration process requires a pair of voltage/current sensors for each panel, which introduces costs, increases size and reduces reliability of the installation. A suitable solutions for reducing the number of sensors is to adopt image-based solution to estimate the electrical characteristics of the PV panels, but the lack of reliable data with large diversity of irradiance and shading conditions is a major problem in this topic. Therefore, this paper presents dataset correlating RGB images and electrical data of PV panels with different irradiance and shading conditions. The dataset was designed to support the design of image-based estimators of electrical data, which could be used to replace large arrays of sensors. The paper also describes the measurement platform used to collect the data, which helps to replicate the experiments in different geographical locations.</p>
Feasibility of hybrid in-stream generator–photovoltaic systems for Amazonian off-grid communities
<p>While there have been efforts to supply off-grid energy in the Amazon, these attempts have focused on low upfront costs and deployment rates. These "get-energy-quick" methods have almost solely adopted diesel generators, ignoring the environmental and social risks associated with the known noise and pollution of combustion engines. Alternatively, it is recommended, herein, to supply off-grid needs with renewable, distributed microgrids comprised of photovoltaics (PV) and in-stream generators (ISG). Utilization of a hybrid combination of renewable generators can provide an energetically, environmentally, and financially feasible alternative to typical electrification methods, depending on available solar irradiation and riverine characteristics, that with community engagement allows for a participatory codesign process that takes into consideration people's needs. A convergent solution development framework that includes designers—a team of social scientists, engineers, and communication specialists—and communities as well as the local industry is examined here, by which the future negative impacts at the human–machine–environment nexus can be minimized by iterative, continuous interaction between these key actors.</p>
Process simulation-based inventory data for the perovskite single-junction, Silicon (PERC) and four-terminal perovskite/silicon tandem solar photovoltaic system life cycles
<p>Process simulation-based inventory data (mass and energy balances) for the perovskite single-junction, silicon (PERC architecture), and four-terminal perovskite/silicon tandem solar photovoltaic system life cycles. The file "0 Overview of simulation flowsheets.xlsx" contains images of the 11 flowsheets that constitute the perovskite/silicon tandem simulation model, which encompasses the perovskite single-junction and silicon (PERC) simulation models. For each unit process shown in each of the flowsheet images, the corresponding Excel file in this repository (with the same name) contains the detailed mass and energy balances, as well as full compositions and thermochemical properties of all streams and the compounds in them. That is, streams are not assumed to consist of pure elements simply moving through the system together, but rather taking into account that streams consist of compounds in solution, which have different thermochemical properties than simple mixtures of the elements involved.</p> <p>Nine additional data files, the names of which start with "Inventory - " contain summarized inventory data for the production of 1000 perovskite single-junction, silicon (PERC), and silicon/perovskite tandem PV modules, each with no Si recycling (i.e. zero circularity), 50% Si recycling, and 100% Si recycling (i.e. full Si circularity).</p>
SolarWalk Dataset: Occupant Identification using Indoor Photovoltaic Harvester Output Voltage
<p>We present the dataset containing time-series open circuit output voltage traces of indoor photovoltaic cell corresponding to occupant door crossing events to perform smart home occupant identification. We collect shadow patterns of five participants from two different doors in two rooms of a building. We collect a total of 900 door entry and exit events during different hours of the day. We sample the voltage at 50 hz and provide the raw timestamped data. We also pre-process the data to filter the event of interest and label the data with occupant id and type of door events. We provide two example scripts to demonstrate how to process raw data and apply machine learning models for occupant identification using solar cell voltage samples. </p>
Monthly production and open-circuit string voltage measurements after 10-year operation of three photovoltaic plants in Southern Spain affected by severe potential-induced degradation
<p>Data are formated in a spreadsheet file. Data are presented from three photovoltaic plants in Southern Spain (Córdoba province - Northern Andalucía) designed and installed by the same person, with the same photovoltaic module and the same model of inverter, deployed at the same time (end 2009). The plants are severely affected by potential-induced degradation (PID), so that secondary effects produce some by-pass diodes to activate, producing three families of open-circuit voltage (Voc) in the modules (~40V), (~26V) and (~12V) of a total of nominal Voc of 42,6V. </p> <p>Monthly production is shown along 11 years (2010-2021), and the measurements of the open-circuit voltage of the strings after 10 years of operation along with the voltage range of the modules of each string.</p> <p>Sheet 1: configuration of the architecture of the three photovoltaic plants and the features of the photovoltaic module installed.</p> <p>Sheet 2: energy production for 11 years of opetation. In the last years some recovery is shown in plant 1 and 2 because a repowering project.</p> <p>Sheet 3: partial climate data of the towns were the plants are located.</p> <p>Sheet 4: open-circuit voltages of the strings and number of modules in each string with open-circuit voltage in the ranges (~40V) and (<=26V) in plant 1, july-2018.</p> <p>Sheet 5: open-circuit voltages of the strings and number of modules in each string with open-circuit voltage in the ranges (~40V), (~26V) and (~12V) in plant 2, july-2020.</p>
Datasets used in the publication 'Site Identification and Power Production for Floating Photovoltaics in The Bahamas'.
<p>This file contains the kml files and Simulating Waves Nearshore numerical wave model data subset used in the publication 'Site Identification and Power Production for Floating Photovoltaics in The Bahamas'.</p>
An Image-Based Gamut Analysis of Translucent Digital Ceramic Prints for Coloured Photovoltaic Modules: Supplementary Data
<p>Colouring the frontglass of PV modules via digital ceramic printing aids in concealing the PV when integrated into existing building façades as BIPV, while admitting sufficient light to produce electricity. This promotes the visual acceptance and adoption of PV as a source of renewable energy in urban environments. The effective colour of the PV laminate is a combination of the transparent colour on glass and the colour of the PV cells. This colour should ideally match the architect’s visual expectations in terms of fidelity, but also in terms of relative PV efficiency as a function of print density. In practice, these requirements are often contradictory, particularly for vivid colours, and the visual results may deviate significantly. This paper presents an objective analysis of how colours appear on PV frontglass laminated with a PV module, using an image-based colour acquisition process. Given a set of 1044 nominal colours uniformly distributed in the RGB colour space, each printed in 10 opacities, we quantify the range of effective colours observed when printed on glass and combined with PV, and their deviation from the nominals. Our results confirm that the effective colour gamuts are significantly constrainted and skewed, depending on the ink volume and glass finish used for printing. In particular, blue-magenta hues cannot be reliably rendered with this process. These insights can serve as guidelines for selecting target colours for BIPV that can be well approximated in practice.</p>
Performance Indicators of Photovoltaic Heat Pumps
<p>Qualitative and quantitaive results included in the review 'Performance Indicators of Photovoltaic Heat Pumps' (DOI:10.1016/j.heliyon.2019.e02691) are given, together with the original data and the corresponding calculations. The files with the simulation info for the SISIFO simulation tool are also available. </p>
Dataset for evaluating rooftop photovoltaic solar panels impact on urban temperature at city scale
<p>This dataset contains simulation outputs from the WRF/BEP+BEM v4.3.3 model, evaluating the impact of rooftop photovoltaic solar panels on urban temperatures in five cities: Kolkata, Austin, Sydney, Athens, and Brussels. The model uses one parent domain and two nested domains with resolutions of 18 km, 6 km, and 2 km, focusing on the 2 km resolution. Simulations cover the summer periods of each city, including panel surface temperature and urban state variables data. The dataset represents diverse climate types: tropical wet and dry (Kolkata), Mediterranean (Athens), moderate oceanic<em> </em>(Brussels), and subtropical humid (Sydney and Austin).</p>
Photovoltaic generation data, for 3 years, regarding the 2022-3 Competition on solar generation forecasting
<p>These data were released under the 2022-3 Competition on solar generation forecasting.</p> <p>Please check our competitions: <a href="http://www.gecad.isep.ipp.pt/smartgridcompetitions">www.gecad.isep.ipp.pt/smartgridcompetitions</a></p> <p> </p> <p>The data set comprises the power generated by photovoltaic panels and data collected from a near weather station. The data was collected in 5 minutes periods. Data comprises:</p> <ul> <li>Hour</li> <li>Starting minute (inclusive) </li> <li>Ending minute (exclusive) </li> <li>Generated power (kW)</li> <li>Temperature (ºC) </li> <li>Dewpoint (ºC)</li> <li>Pressure (hPa) </li> <li>Wind Direction (Degrees)</li> <li>Wind Speed (KM/h)</li> <li>Wind Speed Gust (KM/h)</li> <li>Humidity (%)</li> <li>Hourly Precipitation (mm)</li> <li>Daily rain (mm)</li> <li>Solar Radiation (Watts/m2)</li> </ul> <p><br>The data set represents raw data without any treatment, this means that it is possible to find errors. Data can have missing data or missing reading periods, and a fixed zero (0) value, indicating a failure in the system readings.</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>
Data about of the performance of a building attached photovoltaic panel on different orientations_Ecuador.
<p>Data about of the performance of a building attached photovoltaic panel on different orientations_Ecuador.</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>
A complete energy community dataset with photovoltaic generation, battery energy storage systems and electric vehicles (v1.5)
<p>This dataset represents a complete European energy community based on actual data. In this scenario, a community of 250 households was built using real energy consumption and solar generation data obtained in homes throughout Europe. In total, 200 community members were assigned solar generation, while 150 were assigned a battery storage system. From the acquired sample, new profiles were created and randomly assigned to each end-user while also receiving two electric cars with information on their capacity, state-of-charge, and usage. Furthermore, it is provided the electric vehicle chargers’ information on their location, type, and cost of operation.</p> <p> </p> <p>Version 1.5 update: <span>on the Sheet EVs, lines 29 (Capacity kW), 30 (Charge kW), and 31 (Discharge kW) were updated to the correct values.</span></p> <p> </p> <p>This work has been published in Elsevier's Data in Brief journal:<br><em> Ricardo Faia, Calvin Goncalves, Luis Gomes, Zita Vale<br> Dataset of an energy community with prosumer consumption, photovoltaic generation, battery storage, and electric vehicles<br> Data in Brief, 2023, 109218, ISSN 2352-3409<br> <a href="https://doi.org/10.1016/j.dib.2023.109218.">https://doi.org/10.1016/j.dib.2023.109218</a><br> (<a href="https://www.sciencedirect.com/science/article/pii/S2352340923003372)">https://www.sciencedirect.com/science/article/pii/S2352340923003372)</a></em></p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Data in Brief publication to cite this work.</p> <p> </p> <p>Reference data used to create this dataset:</p> <ul> <li>Filtered energy profiles and renewable energy production profiles: <a href="../record/6778401">https://zenodo.org/record/6778401</a></li> </ul> <ul> <li>Battery storage systems and electric vehicles: <a href="../record/4737293">https://zenodo.org/record/4737293</a></li> </ul>
An ecological network approach to assessing the site suitability of photovoltaic power stations
Open the record for dataset details and reuse information.
Feasibility of hybrid in-stream generator–photovoltaic systems for Amazonian off-grid communities
Open the record for dataset details and reuse information.
Supplement to "Dynamic model of photovoltaic module temperature as a function of atmospheric conditions"
<p>This dataset contains data from two measurement campaigns in autumn 2018 and summer 2019 that were part of the BMWi project "MetPVNet", and serve as a supplement to the paper "Dynamic model of photovoltaic module temperature as a function of atmospheric conditions", published in the special edition of "Advances in Science and Research", the proceedings of the 19th EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2019.</p> <p>Data are resampled to one minute, and include:</p> <ol> <li>PV module temperature</li> <li>Ambient temperature</li> <li>Plane-of-array irradiance</li> <li>Windspeed</li> <li>Atmospheric thermal emission</li> </ol> <p>The data were used for the dynamic temperature model, as presented in the paper</p>
Photovoltaics for agricultural irrigation
<p>This video-abstract is one of the dissemination activities of the H2020 project GRECO under the topic Photovoltaics for Agriculture.</p> <p>The video includes a description of photovoltaic irrigation systems in general and explains the irrigation solutions obtained along the project for large-power photovoltaic irrigation systems.</p> <p>The video will be the basis for the final showroom on PV solutions for irrigation. Irrigators from different countries will attend the showroom in order to have the civil society validation of the solution developed.</p>
Nanoscale Phase Segregation in Supramolecular π‑Templating for Hybrid Perovskite Photovoltaics from NMR Crystallography
<p>Raw and processed NMR data, molecular dynamics data, structure files, photovoltaic data, and additional characterisation data for Nanoscale Phase Segregation in Supramolecular π‑Templating for Hybrid Perovskite Photovoltaics from NMR Crystallography, DOI: 10.1021/jacs.0c11563. For more details see README file. </p> <p>NMR_data.zip: Raw and processed NMR data in the file structure of the TopSpin software, which is available from Bruker. </p> <p>NMR_calculations.zip: The input and output Quantum Espresso files are given for both the single point (*.scf.in and *.scf.out) and NMR calculations (*.nmr.in and *.nmr.out).</p> <p>cif_files_all_structures.zip: All structures in cif format. </p> <p>MD_*.zip: MD trajectories of (PEA)2PbI4, (FEA)2PbI4 and configuration 1 of (PF)2PbI4. The full trajectories are given in dcd format which can be opened using the VMD software. The trajectories are also shown as movies. </p> <p>PV+characterisation.zip: The data for the photovoltaic analysis, XPS spectra and XRD patterns in Excel format. </p>
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>
The ground-mounted photovoltaic panel in the Piedmont region.
<p>This <strong>Geopackage </strong>(<strong>GPKG</strong>) database contains the <strong>ground-mounted photovoltaic panels</strong> as in the <strong>Piedmont region</strong> (north west of Italy).</p><p>The acquisition of the polygons was carried out from several basemaps (such as Bing, Google Satellite, Openstreetmap, etc) made available through the <strong>QGIS </strong>software as <strong>XYZ </strong>tiles services.</p><p>The (manual) recognition activity has been carried out over the last two years without continuity and neither the correctness of the geometries nor the complexity of the information are guaranteed in any way.</p><p>The <strong>GPKG </strong>contains: the database itself, a QGIS <strong>project</strong>, the <strong>layers </strong>and the used <strong>styles</strong>.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.