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15 results for “photovoltaic generation”
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>
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>
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>
Carbon Nanotube Uptake in Cyanobacteria for Near-infrared Imaging and Enhancing Bioelectricity Generation in Living Photovoltaics
<p>Dataset of the work entitled "Carbon Nanotube Uptake in Cyanobacteria for Near-infrared Imaging and Enhancing Bioelectricity Generation in Living Photovoltaics".</p>
Dataset on PowerWorld Software Power Flow Calculations on an Underground Distribution Feeder for Inserting Renewable Distribution Generation from Biogas, Photovoltaic and Small Wind Sources
<p>This Dataset brings all the information, details and source files used for power flow studies of the USP-105 underground feeder of the distribution medium voltage network in the University of São Paulo campus, which has received several embedded DG sources, namely a biogas plant, photovoltaic units and a small wind turbine.</p> <p>The power flow simulations were realized using the PowerWorldTM Simulator, v.23</p> <p>The files types on the Dataset are: </p> <p>.pwb, .pwd and tsb: Powerworld software input files for the simulations</p> <p>.csv: where a semicolon symbol (;) is used as a column separator, while a dot symbol (.) represents the decimal separator. The first row of each CSV file corresponds to the header row to help identify data.</p>
A high-resolution three-year dataset supporting rooftop photovoltaics (PV) generation analytics
Open the record for dataset details and reuse information.
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>
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>
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>
Feasibility of hybrid in-stream generator–photovoltaic systems for Amazonian off-grid communities
Open the record for dataset details and reuse information.
Supplementary Material for "How do seasonal and technical factors affect generation efficiency of photovoltaic power plants?"
<p>Supplementary material for the paper "How do seasonal and technical factors affect generation efficiency of photovoltaic power plants?".</p><p>This supplementary information provides:</p><ul><li>Table S1. Monthly solar irradiation for each plants</li><li>Table S2. Summary of input and output factors, and efficiency scores</li><li>Table S3. Rainy season of the northern Kyushu region</li><li>Table S4. Summary of parameters for the regression model</li><li>Table S5. Average monthly solar irradiation for three cities</li><li>Figure S1. Regression line for each of the PV power plants</li></ul>
Photovoltaic Generation and Load Demand Datasets with 30 seconds resolution from an Actual Prosumer in Cyprus
<p>Real-life datasets regarding the photovoltaic generation (active and reactive power) and the load demand (active and reactive power) from an actual residential prosumer (consumer with a rooftop photovoltaic system) in Cyprus. </p> <p>The residential building, with two occupants and a 200 m<sup>2</sup> approximately indoor area, is located in Nicosia, Cyprus. The building is equipped with a rooftop photovoltaic system consist of a 5 kVA Solar Edge Inverter (SE5K), integrating 18 x REC310PE72 PV panels. The PV panels are installed with 3<sup>o</sup> inclination angle (almost flat) an 190<sup>o</sup> azimuth angle (almost south direction). The building is using split-unit air-conditioners to cover the cooling needs during the summer and a heat-pump underfloor heating system to cover the heating needs during winter. </p> <p>The datasets regarding the photovoltaic generation and the load consumption is captured through the WiseWire Energy Box (local hub) and WiseWire Cloud Platform (http://wisewiresolutions.com/) with a resolution of 30 seconds. It is noted that the photovoltaic generation is taken through the inverter's Modbus interface while the load consumption is obtained through the Modbus interface of Janitza UMG 604 fast reporting smart meter.</p> <p>A total of 24 daily profiles are provided (1 daily profile each month from October 2021 until September 2023, while the exact date is indicated by the ".csv" file name considering the following format yyyy-mm-dd). </p> <p>It should be noted that these profiles has been used for the integration of the Cyprus power system digital twin. In particular, an accurarate and high resolution simulation model of the Cyprus power system has been developed and executed in a real time simulator, where field data from various sources (e.g., PMUs, smart meters, SCADA) are fed in order to replicate the operating conditions of the actual system. Therefore, the datasets provided here, are examples of time-series profiles that have been used to replicate the photovoltaic generation and load consumption of a residential building emulated within the digital twin. More information about the digital twin can be found in Deliverable D8.3 of the OneNet project (<a href="https://onenet-project.eu/wp-content/uploads/2023/12/OneNet_D8.3_V1.0.pdf">OneNet_D8.3.pdf </a>). Moreover, these datasets have been used to develop realistic pre-piloting setups for the Smart5Grid project to preliminary examine pilot use cases in a digital twin based hardware in the loop environment in Deliverable D3.4 (<a href="https://smart5grid.eu/wp-content/uploads/2023/03/Smart5Grid_WP3__D3.4_PU_Smart5Grid-platform-integration-and-HIL-testing-activities_V1.0.pdf">Smart5Grid_D3.4.pdf</a>) of the corresponding project. </p> <p> </p> <p> </p>
Supplementary Information datasets: Evaporation Reduction and Energy Generation Potential using Floating Photovoltaic Power Plants on the Aswan High Dam Reservoir
<p>These files contain supplementary information regarding the "Evaporation Reduction and Energy Generation Potential using Floating Photovoltaic Power Plants on the Aswan High Dam Reservoir"</p>
Wind and photovoltaic power generation lulls in New South Wales (Australia) from 1979 till 2020
<p>Binary file containing univariate and joint exceedance of the 24-hour running means of 100m windspeed and surface solar radiative downward flux (ssrd) for the 95th percentile of the respective areal mean taken over renewable energy development areas in New South Wales, Australia (base period 1 Jan 1979- 31 Dec 2020). Data is derived from the 5th generation ECMWF atmospheric reanalysis of the global climate (ERA5).</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)
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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.