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96 results for “Photovoltaic”
Data from: Evaluating the influence of novel charge transport materials on the photovoltaic properties of MASnI3 solar cells
<p>In recent decades, substantial advancements have been made in photovoltaic technologies, leading to impressive power conversion efficiencies exceeding 25% in perovskite solar cells (PSCs). Tin-based perovskite materials, characterized by their low band gap (1.3 eV), exceptional optical absorption, and high carrier mobility, have emerged as promising absorber layers in PSCs. Achieving high performance and stability in PSCs critically depends on the careful selection of suitable charge transport layers (CTLs). This research investigates the effects of five copper-based hole transport materials and two carbon-based electron transport materials in combination with methyl ammonium tin iodide (MASnI<sub>3</sub>). The carbon-based CTLs exhibit excellent thermal conductivity and mechanical strength, while the copper-based CTLs demonstrate high electrical conductivity. The study comprehensively analyzes the influence of these CTLs on PSC performance, including band alignment, quantum efficiency, thickness, doping concentration, defects, and thermal stability. Furthermore, a comparative analysis is conducted on PSC structures employing both p-i-n and n-i-p configurations. The highest-performing PSCs are observed in the inverted structures of CuSCN/MASnI3/C60 and CuAlO<sub>2</sub>/MASnI<sub>3</sub>/C<sub>60</sub>, achieving power conversion efficiencies (PCE) of 23.48% and 25.18%, respectively. Notably, the planar structures of Cu<sub>2</sub>O/MASnI<sub>3</sub>/C<sub>60</sub> and CuSbS<sub>2</sub>/MASnI<sub>3</sub>/C<sub>60</sub> also exhibit substantial PCE, reaching 20.67% and 20.70%, respectively.</p>
Global Photovoltaic Solar Panel Dataset from 2019 to 2022
<p>Using Google Earth imagery and 2019-2022 Sentinel-2 datasets, we developed a two-stage classification framework to obtain the annual global dataset of solar photovoltaic panels at 20-meter resolution from 2019 to 2022.</p> <p>To classify the global solar photovoltaic panels, we applied the global zoning method of IPCC AR6 WGI to define the main-zoning, and used 4 degree × 4 degree grids to create the sub-zoning. Then, we extracted the solar photovoltaic panels in each sub-zoning, and stored the result data in TIFF format.</p> <p>The number of each file corresponds to the ID in the attribute table of the sub-zoning-ID file, and users can download and use the corresponding file based on the sub-zoning-ID. After the ID, 2019, 2020, 2021, and 2022 respectively represent four years.</p> <p>The folder of the annual global PV dataset is named after the year, and each file is named as "sub zoning ID_year".</p>
Supporting molecular simulations data for "A universal co-solvent dilution strategy enables facile and cost-effective fabrication of perovskite photovoltaics"
<p>Supplementary data for "A universal co-solvent dilution strategy enables facile and cost-effective fabrication of perovskite photovoltaics"</p>
Photovoltaic Windows to Offset the Intensive Energy and Carbon Footprints of Highly Glazed Buildings
<p>Data generated by extensive building energy simulations to determine the impact of next-generation glazing technologies.</p>
Data and code for Article "High-Performance Large-Scale-Integration Organic Phototransistors based on Photovoltaic Nanocells"
<p>The data for figures in the article “High-Performance Large-Scale-Integration Organic Phototransistors based on Photovoltaic Nanocells”. The article <span>DOI: 10.1038/s41565-024-01707-0</span></p>
Networks files of main scenarios analysed in "Distributed photovoltaics provides key benefits for a highly renewable European energy system"
<p>This repository contains the network files (.nc) of the main scenarios (A, B, C, and D) used for analysis in the paper. The code for reproducing these files plus other network files used for sensitivity analysis plus the Jupyter notebooks used for creating all the figures in the paper are available at: https://github.com/Parisra/Distributed-PV-paper </p>
CABRISS - Photovoltaic Waste & Recycling Circular Economy
<p>CABRISS short video presentation.</p>
The photovoltaic inventory dataset in Japan detected in Sentinel-2 imagery
Open the record for dataset details and reuse information.
SQL database with detailed characteristic measurement results of photovoltaic modules that were subjected to accelerated aging sequences
<p>All measurement results are organized in an optimized database, which forms the information base for setting up models for climate sensitive ageing and degradation processes/mechanisms. The database is structured around the modules (module = device under test), see general scheme given in <a href="https://doi.org/10.1002/pip.3090">https://doi.org/10.1002/pip.3090</a>. Modules are logically connected via their specific ageing module groups with strictly associated ageing actions and instances. As stated above, a set of three identical modules is stored together in each specific ageing action (= accelerated ageing test as described in detail in Table <a title="Link to table" href="https://onlinelibrary.wiley.com/doi/10.1002/pip.3090#pip3090-tbl-0001">1</a> of <a href="https://doi.org/10.1002/pip.3090">https://doi.org/10.1002/pip.3090</a>) in order to increase the statistical reliability. Those triples are logically grouped in the database with the corresponding acquired measurement results being canonicalized and stored in the database as well. For future applications (modelling), all measurement information is kept as complete as possible; aggregation is avoided.</p> <a href="https://onlinelibrary.wiley.com/cms/asset/d5235350-b448-4094-b022-3b572d4c9317/pip3090-fig-0001-m.jpg" target="_blank" rel="noopener"></a>
GIS files for photovoltaic plant localization
<p>List of files generated for research related to the location of photovoltaic plants.</p>
Renewable energies and biodiversity: impact of ground-mounted solar photovoltaic sites on bat activity
<ol> <li>Renewable energy is growing at a rapid pace globally, but as yet there has been little research on the effects of ground-mounted solar photovoltaic (PV) developments on bats, many species of which are threatened or protected.</li> <li>We conducted a paired study at 19 ground-mounted solar PV developments in southwest England. We used static detectors to record bat echolocation calls from boundaries (i.e., hedgerows) and central locations (open areas) at fields with solar PV development, and simultaneously at matched sites without solar PV developments (control fields). We used generalized linear mixed-effect models to assess how solar PV developments and boundary habitat affected bat activity and species richness.</li> <li>The activity of six of eight species/species groups analysed was negatively affected by solar PV panels, suggesting that loss and/or fragmentation of foraging/commuting habitat is caused by ground-mounted solar PV panels. <em>Pipistrellus</em> <em>pipistrellus</em> and <em>Nyctalus</em> spp. activity was lower at solar PV sites regardless of the habitat type considered. Negative impacts of solar PV panels at field boundaries were apparent for the activity of <em>Myotis</em> spp. and <em>Eptesicus</em> <em>serotinus</em>, and in open fields for <em>Pipistrellus</em> <em>pygmaeus</em> and <em>Plecotus</em> spp.</li> <li>Bat species richness was greater along field boundaries compared with open fields, but there was no effect of solar PV panels on species richness.</li> <li> <em>Policy Implications</em>: Ground-mounted solar PV developments have a significant negative effect on bat activity, and should be considered in appropriate planning legislation and policy. Solar PV developments should be screened in Environmental Impact Assessments for ecological impacts, and appropriate mitigation (e.g., maintaining boundaries, planting vegetation to network with surrounding foraging habitat) and monitoring should be implemented to highlight potential negative effects.</li> </ol>
Dataset for "Geospatial segmentation of high-resolution photovoltaic production maps for Switzerland", Frontiers Energy
<p>## Notes<br> 1. The following tif files contain the Plain-Of-Array irradiation on south-facing solar panels with different tilts {20,30,40,50,60,70} and during different seasons {summer,winter}.<br> 2. The file pattern is poa\_{season}\_tilt\_{tilt}.tif<br> 3. Read Section 2.2 of the research article for details on how these files were created. <br> 4. These values are POA and need to be converted to energy production via an efficiency factor. This factor was chosen to be a fixed value of 20\% as noted in Section 2.2 of the research article.</p>
Datasets for "Irradiance and cloud optical properties from solar photovoltaic systems" (final version)
<p>This dataset contains all the relevant data for the algorithms described in the paper "<a href="https://amt.copernicus.org/articles/16/4975/2023/">Irradiance and cloud optical properties from solar photovoltaic systems</a>", which were developed within the framework of the <a href="https://www.h-brs.de/de/satelliten-und-meteorologie-unterstuetzte-vorhersage-der-energieerzeugung-von-pv-anlagen-auf">MetPVNet</a> project.</p> <p><strong>Input data:</strong></p> <ol> <li><a href="http://www.cosmo-model.org/">COSMO</a> weather model data (DWD) as NetCDF files (cosmo_d2_2018(9).tar.gz) <ol> <li>COSMO atmospheres for <a href="http://www.libradtran.org/">libRadtran</a> (cosmo_atmosphere_libradtran_input.tar.gz)</li> <li>COSMO surface data for calibration (cosmo_pvcal_output.tar.gz)</li> </ol> </li> <li><a href="https://aeronet.gsfc.nasa.gov/">Aeronet</a> data as text files (MetPVNet_Aeronet_Input_Data.zip)</li> <li>Measured data from the <a href="https://www.h-brs.de/de/satelliten-und-meteorologie-unterstuetzte-vorhersage-der-energieerzeugung-von-pv-anlagen-auf">MetPVNet</a> measurement campaigns as text files (MetPVNet_Messkampagne_2018(9).tar.gz) <ol> <li>PV power data</li> <li>Horizontal and tilted irradiance from pyranometers</li> <li>Longwave irradiance from pyrgeometer</li> </ol> </li> <li>MYSTIC-based lookup table for translated tilted to horizontal irradiance (gti2ghi_lut_v1.nc)</li> </ol> <p><strong>Output data:</strong></p> <ol> <li>Global tilted irradiance (GTI) inferred from PV power plants (with calibration parameters in comments) <ol> <li>Linear temperature model: MetPVNet_gti_cf_inversion_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_gti_cf_inversion_results_faiman.tar.gz</li> </ol> </li> <li>Global horizontal irradiance (GHI) inferred from PV power plants <ol> <li>Linear temperature model: MetPVNet_ghi_inversion_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_ghi_inversion_results_faiman.tar.gz</li> </ol> </li> <li>Combined GHI averaged to 60 minutes and compared with COSMO data <ol> <li>Linear temperature model: MetPVNet_ghi_inversion_combo_60min_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_ghi_inversion_combo_60min_results_faiman.tar.gz</li> </ol> </li> <li>Cloud optical depth inferred from PV power plants <ol> <li>Linear temperature model: MetPVNet_cod_cf_inversion_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_cod_cf_inversion_results_faiman.tar.gz</li> </ol> </li> <li>Combined COD averaged to 60 minutes and compared with COSMO and APOLLO_NG data <ol> <li>Linear temperature model: MetPVNet_cod_inversion_combo_60min_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_cod_inversion_combo_60min_results_faiman.tar.gz</li> </ol> </li> </ol> <p><strong>Validation data:</strong></p> <ol> <li>COSMO cloud optical depth (cosmo_cod_output.tar.gz)</li> <li>APOLLO_NG cloud optical depth (MetPVNet_apng_extract_all_stations_2018(9).tar.gz)</li> <li>COSMO irradiance data for validation (cosmo_irradiance_output.tar.gz)</li> <li><a href="https://www.soda-pro.com/web-services/radiation/cams-radiation-service">CAMS</a> irradiance data for validation (CAMS_irradiation_detailed_MetPVNet_MK_2018(9).zip)</li> </ol> <p><strong>How to import results:</strong></p> <p>The results files are stored as text files ".dat", using Python multi-index columns. In order to import the data into a Pandas dataframe, use the following lines of code (replace [filename] with the relevant file name):</p> <p>import pandas as pd<br>data = pd.read_csv("[filename].dat",comment='#',header=[0,1],delimiter=';',index_col=0,parse_dates=True)</p> <p>This gives a multi-index Dataframe with the index column the timestamp, the first column label corresponds to the measured variable and the second column to the relevant sensor</p> <p><strong>Note:</strong></p> <p>The output data has been updated to match the latest version of the paper, whereas the input and validation data remains the same as in Version 1.0.0</p>
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>
MASS-IPV - WP 5.1 - Barrier mapping for integrated photovoltaics (IPV)
<p>Intro page to survey for barrier mapping for integrated photovoltaics (IPV) as part of work package 5.1 in the MASS-IPV project.</p>
Renewable energies and biodiversity: impact of ground-mounted solar photovoltaic sites on bat activity
Open the record for dataset details and reuse information.
Data from: Evaluating the influence of novel charge transport materials on the photovoltaic properties of MASnI3 solar cells
Open the record for dataset details and reuse information.
Photovoltaic system power production
<p>The dataset contains data related to the photovoltaic (PV) system installed at Poznan Supercomputing and Networking Center used to power the servers in one of the server rooms. The data come from a 1-month measurement period taken between 03.09.2019-03.10.2019. The PV system consists of 80 PV panels with 20kW peak power. Servers are directly connected to inverters so that they can be supplied directly by the power grid, PV system in the case of sufficient generation or by batteries (75kWh) if energy production is too low. Switching between power sources can be controlled by the user, however, if batteries are discharged below 60%, servers immediately switch to the power grid to extend battery life. During the measurements, only part of the servers was attached to the PV system responsible for ~1kW power usage.</p> <p>Poznan’s coordinates: 52°24'24.91" N 16°55'47.75" E</p> <p>Time period: 03.09.2019-03.10.2019</p>
Data-sets for Indoor Photovoltaic Behavior in Low Lighting condition
<p>This file includes five data-sets from behavior of indoor photovoltaic modules under pure artificial lighting conditions with low light intensity.</p> <p>Three types of PV module are included by use of two types of light sources measured in a high accuracy controlled light testbed.</p> <p>One of data-sets includes data measured within a warehouse as a industrial environment mostly with pure artificial lighting with low light intensity.</p> <p>Each data-set includes multiple measurements at different light intensities and temperature condition.</p> <p>Each measurement includes both radiometry and photometry spectrum of the light, integrative light intensity and temperature in addition to the voltage-current relation of the PV module in that lighting condition.</p>
Making the sun shine at night: Comparing the Cost of Dispatchable Concentrating Solar Power and Photovoltaics with Storage: Balmorel input data revised
<p><strong>Description of the dataset</strong></p> <p>This dataset holds all Balmorel model input data as well as the Balmorel code used for the scenarios of the paper 'Making the Sun Shine at Night: Comparing the Cost of Dispatchable Concentrating Solar Power and Photovoltaics with Storage' submitted to 'Energy Sources, Part B: Economics, Planning, and Policy.'</p> <p><strong>Data format</strong></p> <p>We provide the data in form of the data folders holding the .inc files for all scenarios.</p> <p>The original Balmorel source code is available under https://github.com/balmorelcommunity/Balmorel under the ISC license. It was adapted in order to include a new technology generating electricity from heat (GETOH). The inputs for this development were kindly supported by DTU, Ea Energy Analyses and Jasper Geipel (TU Wien) with previously done works.</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.