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21 results for “photovoltaic data”
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>
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>
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>
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>
Raw data for Ultrathin wide-bandgap a-Si:H based solar cells for transparent photovoltaic applications
<p>In the following the raw data lying the foundation of the paper “Ultrathin wide-bandgap a-Si:H based solar cells for transparent photovoltaic applications” (Lopez-Garcia et al.) published in Solar Rapid Research Letters, DOI: 10.1002/solr.202100909 (2021), are described. They were obtained under the funding provided by the European Union H2020 Framework Programme under Grant Agreement no. 826002 (Tech4Win) and by the Mater-One (Refs. PID 2020-116719RB-C42 and PID 2020-116719RB-C41) and SCALED (Ref. PID 2019-109215RB-C4) projects funded by the Spanish MCIN/AEI/10.13039/5011000110033.</p> <p>UV–vis measurements were acquired with a dual-beam spectrophotometer setup (Perkin Elmer Lambda L35) in transmittance mode (light source and detector normal to sample’s surface (i.e., 0<sup>o</sup>)) and in reflectance mode (with an Integrating sphere) scanning from 300 to 800 nm.</p> <p>J–V measurements under illumination were carried out using a homemade setup consisting on a AAA solar simulator calibrated using a NREL-certified Si reference solar cell (Abet Technologies, Model 15150). Electrical measurements were carried out with a source-measure unit (Keithley 2400) in four-wire sense mode, controlled by the software Tracer (ReRa solutions) using a IEEE 488 GPIB Instrument Control Device (National Instruments GPIB-USB-HS).</p>
Research data supporting "A validated model of a photovoltaic water pumping system for off-grid rural communities"
<p>Research data supporting "A validated model of a photovoltaic water pumping system for off-grid rural communities", Applied Energy, 2019</p>
Real operating data of a photovoltaic system installed at Area Science Park - Trieste - Italy
<p>Data collected from a monocrystalline silicon photovoltaic (PV) plant installed on building Q2 at Area Science Park in the Basovizza campus located in Trieste, Italy. The data represent almost 9 years of real operating conditions of the PV plant. Every 15 minutes the DC side electrical PV system working parameters were recorded, in addition also ambient temperature, irradiance in the plane of the modules and panel temperatures were recorded. Data are periodically downloaded using a control software.</p>
Modelled temperature, mortality impact, and external benefits of cool roofs and rooftop photovoltaics in London - supporting data
<p>Supporting data for "Modelled temperature, mortality impact, and external benefits of cool roofs and rooftop photovoltaics in London"</p> <p>Included are outputs from the Weather Research and Forecast (WRF) model. All simulations cover London, United Kingdom over summer 2018. Scenarios include a "baseline" which represents the real urban climate of the region, and scenarios which model 100% coverage of rooftops with either high albedo materials or solar panels. Data are provided in netCDF format.</p> <ul> <li>The baseline simulation which models the current urban climate of the region WRF_Urb_BouLac_T2-V10-U10_20180525-20180831.nc</li> <li>The 100% rooftop-solar simulation WRF_Urb_BouLac_PV_T2-V10-U10-PSFC-RAINNC-TH2-Q2_20180525-20180831.nc</li> <li>The 100% high-albedo roof simulation WRF_Urb_BouLac_ClRf_T2-V10-U10-PSFC-RAINNC-TH2-Q2_20180525-20180831.nc</li> <li>The non-urban scenario is in WRF_NoUrb_BouLac_T2-V10-U10-PSFC-RAINNC-TH2-Q2_20180525-20180831.nc</li> <li>The power production estimates solarpv_prod_2018.nc</li> <li>wrf_popweighting-main.zip contains the analysis code. It is provided as-is with no guarantee of usability.</li> </ul> <p>T2 means air temperature at 2m height. V10 and U10 are windspeeds at 10m height. PSFC is surface level pressure. TH2 is potential temperature. Q2 is specific humidity at 2m height.</p> <p>More description of the simulations is given in the citing article.</p> <p> </p>
Data and results related to "Fattori et al. 2017 - High Solar Photovoltaic Penetration in the Absence of Substantial Wind Capacity: Storage Requirements and Effects on Capacity Adequacy - Energy"
<p>The file includes data used for the analysis and results coming from the study (which was focused on the Italian "Nord" bidding zone). In particular:</p> <p>(i) Series of hourly load data [MW], from 01.01.2006 to 31.12.2015. The data come from elaborations based on ENTSO-E (https://www.entsoe.eu/db-query/country-packages/production-consumption-exchange-package) and Terna S.p.A. (http://www.terna.it/en-gb/sistemaelettrico/transparencyreport/load/actualload.aspx). All the elaborations are described in details on the paper.</p> <p>(ii) Data related to the penetration of PV. Installed capacity of PV is assumed to increase from zero up to the capacity needed so that the average annual PV generation (based on the years 1986-2015) potentially equals the average annual demand (based on the years 2006-2015).</p> <p>(iii) Synthesis of the results about: residual load (with and w/o storage), ramps (with and w/o storage), excess energy (with and w/o storage), storage requirements</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>
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>
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>
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>
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>
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>
Data from: Evaluating the influence of novel charge transport materials on the photovoltaic properties of MASnI3 solar cells
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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>
Data from: Incorporation of Mn2+ into CdSe quantum dots by chemical bath co-deposition method for photovoltaic enhancement of quantum dot-sensitized solar cells
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