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325 results for “PV”
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>
Test-bed PV system performance data
<p>The data is generated from the on-site data acquisition devices installed at the outdoor testing facilities of the Smart Energy Infrastructure | PHAETHON CoE. </p>
Assessing the Provision of a Multi-Ancillary Service Framework provided by PV-BES Systems (ProMiSe) - LAB Experiments
<p>This dataset comprises the results obtained during ProMiSe project. In particular, scope of ProMiSe is to test and validate a new unified control architecture used to overcome voltage violations and voltage unbalance issues by conducting real-time simulations and utilizing a power hardware-in-the-loop platform. The validation scheme reflects a range of different scenarios and realistic test cases of integration of the unified control strategy in modern distribution networks. In particular, three experiments are considered:</p> <p>- <strong>Experiment 1</strong>: The performance of the proposed unified strategy is evaluated in a low-voltage (LV) distribution network (DN). The LV DN, the detailed model of a three-phase, four-leg converter (3Ph4LC) and the deployed voltage regulation (VR) / voltage unbalance mitigation (VUM) algorithms are developed in the real time digital simulator (RTDS).</p> <p>- <strong>Experiment 2</strong>: The performance of the proposed unified strategy is evaluated in the LV DN of Experiment 1. The LV DN, the detailed model of a 3Ph4LC and the deployed VR/VUMalgorithms are developed in the RTDS. Compared to Experiment 1, in Experiment 2 the line <em>R/X</em> ratio is decreased, in order to demonstrate the impact of the <em>R/X</em> on the provided ancillary services. Here, it should be highlighted that grid voltage levels as well as the power levels of load nodes have been properly readjusted to achieve similar voltage levels to the point of the inter-connection (POI) of the 3Ph4LC with Experiment 1. </p> <p>- <strong>Experiment 3</strong>: The performance of the proposed unified strategy is evaluated in the LV DN of Experiment 1. The LV DN and the VR/VUM algorithms are developed in the RTDS. Compared to Experiment 1, in Experiment 3 the detailed model of the ProMiSe 3Ph4LC has been replaced with controllable current sources. Note that the filter has been remained in the analysis. Here, it should be highlighted that grid voltage levels have been properly readjusted to achieve similar voltage levels to POI with Experiment 1. In addition, due to a resonance problem observed during the test between the lab equipment and the virtual network in the software platform, the filter parameters are readjusted to overcome the problem.</p> <p>For each experiment, the obtained results are provided in csv format.</p> <p>For more Information regarding the ProMiSe project, the conducted simulations, as well as the files/variables expanation, please refer to ProMiSe-Info.pdf.</p>
Open Science: New Challenges and Opportunities for the PV sector
<p>Presentation given at the European PV Solar Energy Conference, Marseille, 2019 about the development of Open Science in the context of photovoltaics</p>
QTL Mapping for Resistance to Cankers Induced by Pseudomonas syringae pv. actinidiae (Psa) in a Tetraploid Actinidia chinensis Kiwifruit Population
<p>Raw Illumina R1 sequence reads for individual plants genotyped for the study entitled "QTL Mapping for Resistance to Cankers Induced by <em>Pseudomonas syringae</em> pv. <em>actinidiae</em> (Psa) in a Tetraploid <em>Actinidia chinensis</em> Kiwifruit Population" accepted in MDPI Pathogen journals, Special issue <em>"</em><em>Pseudomonas syringae</em> Species Complex"</p>
QTL Mapping for Resistance to Cankers Induced by Pseudomonas syringae pv. actinidiae (Psa) in a Tetraploid Actinidia chinensis Kiwifruit Population
<p>Raw Illumina R2 sequence reads for individual plants genotyped for the study entitled "QTL Mapping for Resistance to Cankers Induced by <em>Pseudomonas syringae</em> pv. <em>actinidiae</em> (Psa) in a Tetraploid <em>Actinidia chinensis</em> Kiwifruit Population" accepted in MDPI Pathogen journals, Special issue <em>"</em><em>Pseudomonas syringae</em> Species Complex"</p>
Data from PV module energy rating standard IEC 61853-3 intercomparison
<p>This is the data from PV module energy rating standard IEC 61853-3 intercomparison.</p> <p>Details can be found in:</p> <p>M. R. Vogt, S. Riechelmann, A. M. Gracia-Amillo, A. Driesse, A. Kokka, K. Maham, P. Kärhä, R. Kenny, C. Schinke, K. Bothe, J. C. Blakesley, E. Music, F. Plag, G. Friesen, G. Corbellini, N. Riedel-Lyngskær, R. Valckenborg, M. Schweiger, W. Herrmann, „PV module energy rating standard IEC 61853-3 intercomparison and best practice guidelines for implementation and validation”, accepted IEEE JPV. DOI (identifier) 10.1109/JPHOTOV.2021.3135258</p>
H2020 Platone Greek Demonstrator PV_generation_20190227_20200506
<p>This dataset contains PV generation (kWh) of 7 producers (connected to the medium voltage (MV) level, 20kV) from 27/02/2019 to 06/05/2020 in 15 min intervals and contains the following fields:</p> <p>Customer_id</p> <p>Value_KWh</p> <p>Timestamp</p>
Genome assemblies of Xanthomonas oryzae pv. oryzae (PXO35, FXO38, Huang604) and Xanthomonas oryzae pv. oryzicola (BAI35, MAI23)
<p>Genome assemblies of <em>Xanthomonas oryzae</em> pv. <em>oryzae</em> (<em>Xoo</em>) and <em>Xanthomonas oryzae</em> pv. <em>oryzicola</em> (<em>Xoc</em>). Genome assemblies of the <em>Xoo</em> strains PXO35, FXO38, Huang604 and the <em>Xoc</em> strains BAI35, MAI23, have been generated with Flye based on ONT reads. For each of these strains, we corrected the sequences encoding for transcription activator-like effectors (TALEs) with our TALE-correction pipeline (https://github.com/Jstacs/Jstacs/tree/master/projects/talecorrect). For Xoo PXO35, we additionally provide assemblies based on reads obtained from different sequencing methods (Illumina, PacBio, ONT) generated by a collection of (hybrid) assembly strategies and different polishing approaches applied to combinations of these.</p>
Dataset for Local Communication in Small-Scale PV Systems: Study on Inverter - Smart Meter PLC Communication
<p>This study investigates communication technologies and protocols for small-scale photovoltaic (PV) systems, focusing on the interaction between inverters and smart meters. The research evaluates the performance of Power Line Communication (PLC) technologies, comparing both narrowband (NB-PLC) and broadband (BB-PLC) options. The analysis identifies MODBUS protocol limitations and highlights the benefits of advanced protocols like DLMS/COSEM and DNP3 for enhanced efficiency and reliability. Field tests demonstrate the viability of PLC for residential PV systems, with narrowband PLC showing better performance over longer distances. Future work aims to optimize PLC communication, digitize ripple control signals, and develop a Multi-Radio and Cable Access Technology (Multi-RCAT) module. This module will integrate various communication technologies, enabling flexible and redundant local communication behind utility sub-meters. These advancements will support real-time production and consumption control, contributing to the efficient and sustainable operation of decentralized energy systems.</p>
Testing data from SUPER PV demo site in Vilnius (Lithuania)
<p>Sets of the data, collected with SuperPV MLPE boxes from the Lithuanian demo site during the period 2021/08/15 - 2021/08/30. Contain information of the PV modules parameters, as follows:</p> <p>"1122334455667788" - Nr.1 / 90-degree angle (vertical);<br> "FFFFFFFFFFFFFFFF" - Nr.2 / 90-degree angle (vertical);</p> <p>"3333333333333333" - Nr.3 / 45-degree angle;<br> "4444444444444444" - Nr.4 / 45-degree angle;</p> <p>Data files column names explanation:</p> <p>Uoc - open circuit voltage<br> Isc - short circuit current<br> Uin - voltage at maximum power point<br> Iin - current at maximum power point</p> <p>Temp - temperature<br> P - power</p>
Energy consumption data from an office building, waterpark and warehouse in Slovenia and energy production data from a PV Plan (900KW)
<p>The first dataset included 9-month hourly energy data from a waterpark, a warehouse and high-rise office buildings. The second dataset includes 10-year hourly energy production data from a PV plant (900KW). Both datasets refer to Ljubljana, Slovenia. </p>
PV and Weather Datasets from Demosites in Oslo, Touzer, and Sevilla
<p>We have gathered data on the power generation of seven different PV modules from three demonstration sites in Oslo, Touzer, and Sevilla for a comparitive analysis. This data was sourced from TIGO cloud for the PV modules and Solcast, an open-source platform, for historical weather information. The data set is spanning from May 2021 to November 2023. These datasets are characterized by high-resolution recordings taken every 5 minutes.</p> <p> </p> <p>Other contributors: <span> Alexander G Ulyashin (SINTEF Industry)</span><span>, Xiang Ma (SINTEF Industry)</span><span>, Alicia Arce (Ayesa Engineering SA)</span><span>, Rebeca Gutierrez (Ayesa Engineering SA) </span><span>, </span><span>Hélène Ben Khemis (Higher Institute of Technological Studies in Touzer, T)</span><span>, Zaher Khantouch (Higher Institute of Technological Studies in Touzer, T) </span><span>and Ahmet Soylu</span> (OsloMet)</p> <p> </p>
PV-gradient (PVG) tropopause: Time series 1980--2017 in four reanalyses
<h1>PV-gradient tropopause time series</h1> <h2>General description</h2> <p>These datasets contain time series of the PV-gradient tropopause (PVG tropopause) introduced by A. Kunz (2011, <a href="https://doi.org/10.1029/2010JD014343">doi:10.1029/2010JD014343</a>) and calculated by K. Turhal (2024, paper " Variability and Trends in the PVG Tropopause", preprint in EGUsphere: https://doi.org/10.5194/egusphere-2024-471).</p> <h2>Data and methods</h2> <p>The PVG tropopause has been computed by means of the Eddy Tracking Toolkit (developed by J. Clemens and K. Turhal, to be published):</p> <ul> <li>from four reanalyses: ERA5, ERA-Interim, MERRA-2 and JRA-55</li> <li>for the time range 1980/01/01 -- 2017/12/31 in time steps of the according reanalyses, i.e. four times daily at 00h, 06h, 12h and 18h</li> <li>on each isentropic level, with potential temperatures (theta) ranging from 320 K to 380 K, in steps of 5 K for ERA5 and 10 K for the other reanalyses.</li> </ul> <h2>Contents</h2> <p>Datasets are provided for each year and isentropic level in NetCDF4 format, every file consisting of two groups for the northern and southern hemisphere. Each group contains the following variables, with time as dimension:</p> <ul> <li>time in seconds since 2000/01/01 00:00 UTC</li> <li>u_lim: Zonal wind speed at the PVG tropopause</li> <li>vh_lim: Horizontal wind speed at the PVG tropopause</li> <li>q_lim: Maximum of Q = vh * Grad PV</li> <li>eqlat_lim: Location of the PVG tropopause in equivalent latitudes</li> <li>latmean_lim: Location of the PVG tropopause in latitudes</li> <li>pv_lim: PV value at the PVG tropopause</li> </ul> <p>In this upload, the PVG tropopause time series are included as *.zip files:</p> <ul> <li>ERA5 dataset: "pvg-tp_era5_ts.zip"</li> <li>ERA-Interim dataset: "pvg-tp_eraint_ts.zip"</li> <li>MERRA-2 dataset: "pvg-tp_merra2_ts.zip"</li> <li>JRA-55 dataset: "pvg-tp_jra55_ts.zip"</li> <li>Plots of time series for each reanalysis of the variables eqlat_lim, latmean_lim and pv_lim: "pvg_tropopause_timeseries_plots.zip".</li> </ul> <h2>How to use</h2> <p>The variables in these netCDF files are grouped by hemisphere. To read in the data, specify the group first ("NorthernHemisphere" or "SouthernHemisphere") and then the variable name (see list above). In Python, this can be done as follows:</p> <pre><code>import netCDF4 as nc file="<insert file path here>" d = nc.Dataset(file) # read in a variable. Syntax: d["group name"]["variable name"][:]. For example: latmean_lim = d["NorthernHemisphere"]["latmean_lim"][:] # test print print(f"First value of latmean_lim in NH: {latmean_lim[0]}")</code></pre> <p>If you would like to read in all variables in both hemispheres, you can loop e.g. as follows:</p> <pre><code>import netCDF4 as nc file = "<insert file path here>" d = nc.Dataset(file) # iterate through both hemispheres for hem in ["NorthernHemisphere", "SouthernHemisphere"]: # select the group to each hemisphere in the netCDF file g = d.groups[hem] # iterate through variables in each hemisphere. "v" is the name of each variable in the group. for v in g.variables: # read in the data for variable 'v' in hemisphere 'hem' as an array var = g[v][:] # just a test print, optional print(f"First value of {v} in {hem.replace('Hem', ' Hem')} is {var[0]}")</code></pre> <h2>Funding</h2> <p>This project has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – TRR 301 – Project-ID 428312742, TPChange: The Tropopause Region in a Changing Atmosphere (<a href="https://tpchange.de/">https://tpchange.de/</a>).</p>
Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America
<p>This dataset provides model-ready data to include geospatial differentiation in solar and wind power investment options in energy models (primarily capacity expansion models and dispatch models) at the level of every Central and South American country. </p> <p>The methodology used to create the dataset takes into account resource quality, land use restrictions, distance from infrastructure, and other factors. It was previously applied to create an all-Africa dataset explained in Sterl et al. (2022) and published by Sterl, Hussain & Elabbas (2023). </p> <p>Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).</p> <p>Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.</p> <p>Folder (3) provides the corresponding shapefiles.</p> <p>Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 3, 5 or 10 depending on country size.</p> <p>Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).</p> <p>Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).</p> <p>Countries/territories included in the dataset: </p> <p>Argentina<br>Belize<br>Bolivia<br>Brazil<br>Chile<br>Colombia<br>Costa Rica<br>Cuba<br>Dominican Republic<br>Ecuador<br>El Salvador<br>French Guiana<br>Guatemala<br>Guyana<br>Haiti<br>Honduras<br>Jamaica<br>Nicaragua<br>Panama<br>Paraguay<br>Peru<br>Suriname<br>Uruguay<br>Venezuela</p> <p> </p> <p><strong>References</strong></p> <p>Sterl, S., Hussain, B., Miketa, A. <em>et al.</em> An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. <em>Sci Data</em> <strong>9</strong>, 664 (2022). <a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></p> <p>Sterl, S., Hussain, B., & Elabbas, M. (2023). Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power » (1.2.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14870967">https://doi.org/10.5281/zenodo.14870967</a></p>
A high-resolution three-year dataset supporting rooftop photovoltaics (PV) generation analytics
<p>This dataset includes measured photovoltaic (PV) power generation data and on-site weather data collected from 60 grid-connected rooftop PV stations in Hong Kong over a three-year period (2021-2023). The PV power generation data was collected at 5-minute intervals. The meteorological data was collected at 1-minute intervals from an on-site weather station. The metadata was represented using Brick schema was developed, which simplifies the data comprehension and the development of smart analytics applications. The detailed Brick model is stored in the .ttl file format, which can be accessed for retrieving metadata through the use of SPARQL queries.This dataset can be used in various applications - PV generation benchmarking, PV degradation analysis, PV fault detection, solar radiation and PV power generation forecasting, and the simulation and design of PV systems.</p>
Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power »
<p>This dataset contains data provided alongside the paper "An all-Africa dataset of energy model “supply regions” for solar PV and wind power" by Sterl et al. (2022).</p> <p>It concerns a novel representative subset of attractive sites for solar PV and onshore wind power for the entire African continent. We refer to these sites as “Model Supply Regions” (MSRs). This MSR dataset was created from an in-depth analysis of various existing datasets on resource potential, grid infrastructure, land use, topography and others (see Methods), and achieves hourly temporal resolution and kilometre-scale spatial resolution. This dataset fills an important research need by closing the gap between comprehensive datasets on African VRE potential (such as the Global Solar Atlas and Global Wind Atlas) on the one hand, and the input needed to run cost-optimisation models on the other. It also allows a detailed analysis of the trade-offs involved in exploiting excellent, but far-from-grid resources as compared to mediocre but more accessible resources, which is a crucial component of power systems planning to be elaborated for many African countries.</p> <p>Five separate datasets are included:</p> <p>Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).</p> <p>Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.</p> <p>Folder (3) provides the corresponding shapefiles.</p> <p>Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 2, 5 or 10 depending on country size.</p> <p>Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).</p> <p>Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).</p> <p>Countries/territories included in the dataset: </p> <p>Algeria<br>Angola<br>Benin<br>Botswana<br>Burkina Faso<br>Burundi<br>Cameroon<br>Central African Republic<br>Chad<br>Congo Republic<br>Democratic Republic of the Congo<br>Djibouti<br>Egypt<br>Equatorial Guinea<br>Eritrea<br>Eswatini<br>Ethiopia<br>Gabon<br>The Gambia<br>Ghana<br>Guinea<br>Guiné-Bissau<br>Côte d'Ivoire<br>Kenya<br>Lesotho<br>Liberia<br>Libya<br>Madagascar<br>Malawi<br>Mali<br>Mauritania<br>Morocco<br>Mozambique<br>Namibia<br>Niger<br>Nigeria<br>Rwanda<br>Senegal<br>Sierra Leone<br>Somalia<br>South Africa<br>South Sudan<br>Sudan<br>Togo<br>Tunisia<br>Uganda<br>Tanzania<br>Zambia<br>Zimbabwe</p> <p> </p> <p><strong>References</strong></p> <p>Sterl, S., Hussain, B., Miketa, A. <em>et al.</em> An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. <em>Sci Data</em> <strong>9</strong>, 664 (2022). <span><a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></span></p> <p><strong>See also</strong></p> <p>Sterl, S. (2024). Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America (1.0.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10650822" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10650822</a></p>
Database of PV output forecast errors
<p>This database is extracted from 180 studies on PV output forecasting and is used for the research paper "What drives the accuracy of PV output forecasts?". The data of 21 key variables including the publishing year of the papers, the error values, data processing techniques used by the models, the length of the test sets, the forecast resolution, the country and region of the studies, the methodology of the forecast models, the forecast horizon, and the error metrics are included. Besides, other information such as the weather condition of the forecasts, the number of power plants, the installed capacity... is also included. </p>
PV electrodes electrochemistry
<p>Electrochemistry of Polyviologens deposited on FTO glass by dropcasting</p>
data for "On the tuning and performance of Stand-Alone Large-Power PV irrigation systems"
<p>These data were used in article "On the tuning and performance of Stand-Alone Large-Power PV irrigation systems" ( <a href="https://doi.org/10.1016/j.ecmx.2021.100175">https://doi.org/10.1016/j.ecmx.2021.100175</a>)</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.