This dataset contains a subset of data from our Building Permit Application dataset. The data has been filtered to only include permit applications for solar (permit type = PVRS).To learn more about solar energy in Cary check out our Solar Energy webpage.This file is created from the Town of Cary permit application data. It has been created to conform to the BLDS open data specification for building permit data (permitdata.org). In the Town of Cary a permit application may result in the creation of several permits. Rows in this table represent applications for permits, not individual permits. Individual permits may be released as a separate dataset. With the exception of a few fields, we have applied all of the required and preferred fields of the required dataset for permits. This data is updated daily.Used as a part of the Solar, Cary, and You Dashboard

The solar array is located on Fire Station 3, located at 1021 Harris Mill Road, Morrisville, NC, 27560. The solar array has produced a peak power of 81.31 kWp This data is updated automatically via an API pull from our selected solar inverter provider daily.

(1) Output of the Renewable Energy Model (REM) as described in Insights into weather-driven extremes in Europe’s resources for renewable energy (Ho and Fiedler, 2024), last modification on 30.10.2023 from Linh Ho, named year_PV_wind_generation_v2.nc, with 23 years from 1995 to 2017. REM includes one simulation of photovoltaic (PV) power production and one simulation of wind power production across European domain, with a horizontal resolution of 48 km, hourly output for the period 1995--2017.

The output has a European domain with the same size as in the reanalysis dataset COSMO-REA6. This is a rotated grid with the coordinates of the rotated North Pole −162.0, 39.25, and of the lower left corner −23.375, −28.375. See Bollmeyer et al. (2014, http://doi.org/10.1002/qj.2486). Data downloaded from https://opendata.dwd.de/climate_environment/REA/COSMO_REA6/

(2) Weather pattern classification daily for Europe from 1995 to April 2020, named EGWL_LegacyGWL.txt, from James (2007, http://doi.org/10.1007/s00704-006-0239-3)

(3) The installation data of PV and wind power in Europe for one scenario in 2050 from the CLIMIX model, processed to have the same horizontal resolution as in REM, named installed_capacity_PV_wind_power_from_CLIMIX_final.nc. Original data were provided at 0.11 degree resolution, acquired from personal communication with the author from Jerez et al. (2015, http://doi.org/10.1016/j.rser.2014.09.041)

(4) Python scripts of REM, including: - model_PV_wind_complete_v2.py: the main script to produce REM output - model_PV_wind_potential_v2.py: produce potential (capacity factor) of PV and wind power for model evaluations, e.g., against CDS and Renewables Ninja data, as descript in Ho and Fiedler (2024) - model_PV_wind_complete_v1_ONLYyear2000.py: a separate Python script to produce REM output only for the year 2000. Note that the data for 2000 from COSMO-REA6 were read in a different approach (using cfgrib) probably due to the time stamp changes at the beginning of the milenium, also explains the larger size of the final output - utils_LH_archive_Oct2022.py: contains necessary Python functions to run the other scripts

(5) Jupyter notebook files to reproduce the figures in Ho and Fiedler (2024), named Paper1_Fig*_**.ipynb

(6) Time series of European-aggregated PV and wind power production hourly during the period 1995--2017, processed data from the dataset (1) to facilitate the reproduction of the figures, including two installations scale-2019 and scenario-2050: - Timeseries_all_hourly_1995_2017_GW_scale2019.csv - Timeseries_all_hourly_1995_2017_GW_scen2050.csv

Description for Solar Power Generation

UNISOLAR dataset contains high-granularity Photovoltaic (PV) solar energy generation, solar irradiance, and weather data from 42 PV sites deployed across five campuses at La Trobe University, Victoria, Australia. The dataset includes approximately two years of PV solar energy generation data collected at 15-minute intervals. Geographical placement and engineering specifications for each of the sites are also provided to aid researchers in modeling solar energy generation. Weather data is available at 1-minute intervals and is provided by the Australian Bureau of Meteorology (BOM). Apparent temperature, air temperature, dew point temperature, relative humidity, wind speed, and wind direction were provided under the weather data. The paper describes the data collection. methods, cleaning, and merging with weather data. This dataset can be used to forecast, benchmark, and enhance operational outcomes in solar sites.

Acknowledgements Please cite the following paper if you use this dataset:

S. Wimalaratne, D. Haputhanthri, S. Kahawala, G. Gamage, D. Alahakoon and A. Jennings, "UNISOLAR: An Open Dataset of Photovoltaic Solar Energy Generation in a Large Multi-Campus University Setting," 2022 15th International Conference on Human System Interaction (HSI), 2022, pp. 1-5, doi: 10.1109/HSI55341.2022.9869474. Usage Policy and Legal Disclaimer This dataset is being distributed only for Research purposes, under Creative Commons Attribution-Noncommercial-ShareAlike license (CC BY-NC-SA 4.0). By clicking on download button(s) below, you are agreeing to use this data only for non-commercial, research, or academic applications. You may need to cite the above papers if you use this dataset.

Github: https://github.com/CDAC-lab/UNISOLAR

Description for Power Consumption Data

About Dataset

UNICON, a large-scale open dataset on University Consumption of utilities, electricity, gas and water. This dataset is publicly released as part of La Trobe University’s commitment to Net Zero Carbon Emissions by 2029, for which we are building the La Trobe Energy AI/Analytics Platform (LEAP) that leverages Artificial Intelligence (AI) and Data Analytics to analyze, predict and optimize the consumption, generation and utilization of electricity, renewables, gas and water resources. UNICON contains consumption data for La Trobe’s five campuses in geographically distributed regions, across four years, 2018-2021 inclusive. This includes the COVID-19 global pandemic timeline of university shutdown and work from home measures that led to a significant decrease in the consumption of utilities. The consumption data consists of smart electricity meter readings at 15-minute granularity, gas meter readings at hourly intervals and water meter readings at 15-minute intervals. UNICON also contains weather data from the closest weather station to each campus, collected at two-speed latency of 1 minute and 10 minutes. The dataset is annotated with internal events of significance, such as energy conservation measures (ECMs) and other measurement and validation (M&V) activities conducted as part of LEAP optimization. To the best of our knowledge, this is the first large-scale, comprehensive, open dataset for the three main utilities, electricity, gas, and water consumption in a multi-campus university setting.

Dataset file descriptions

campus_meta.csv – This file contains information about each campus in the university network.

nmi_meta.csv – Information about NMIs such as campus location and peak demand is listed in this file.

building_meta.csv – This file contains meta information about buildings in each campus which include campus location, floor area and etc.

calender.csv – University calendar for the data collection period is included in this file.

events.csv – There are series of events happened at each building which include energy efficiency projects such as LED installation and HVAC system updates. This file contains the dates related to each event at building level.

nmi_consumption.csv – Consumption data of NMIs are recorded in this file.

building_consumption.csv – Consumption data of buildings are recorded in this file.

building_submeter_consumption.csv – Consumption data of building sub-meters are recorded in this file.

gas_consumption.csv – Gas consumption data of available campuses are recorded in this file.

water_consumption.csv – Water consumption data of available campuses are recorded in this file.

weather_data.csv – Weather data collected from respective weather stations.

Acknowledgements Please cite the following paper if you use this dataset:

H. Moraliyage, N. Mills, P. Rathnayake, D. De Silva and A. Jennings, "UNICON: An Open Dataset of Electricity, Gas and Water Consumption in a Large Multi-Campus University Setting," 2022 15th International Conference on Hu...

Comprehensive dataset containing 84 verified Solar energy system service businesses in North Carolina, United States with complete contact information, ratings, reviews, and location data.

The Historically Black Colleges and Universities (HBCU) Solar Radiation Monitoring Network operated from July 1985 through December 1996. Funded by DOE, the six-station network provided 5-minute averaged measurements of direct normal, global, and diffuse horizontal solar irradiance. The data were processed at NREL to improve the assessment of the solar radiation resources in the southeastern United States. Historical HBCU data available online include quality assessed 5-min data, monthly reports, and plots. In January 1997 the HBCU sites became part of the CONFRRM solar monitoring network and data from the two remaining active stations, Bluefield State College and Elizabeth City State University, are collected by the NREL Measurement & Instrumentation Data Center (MIDC).

Comprehensive dataset containing 85 verified Solar photovoltaic power plant businesses in North Carolina, United States with complete contact information, ratings, reviews, and location data.

This dataset contains all the relevant data for the algorithms described in the paper "Irradiance and cloud optical properties from solar photovoltaic systems", which were developed within the framework of the MetPVNet project.

Input data:

COSMO weather model data (DWD) as NetCDF files (cosmo_d2_2018(9).tar.gz)

COSMO atmospheres for libRadtran (cosmo_atmosphere_libradtran_input.tar.gz)

COSMO surface data for calibration (cosmo_pvcal_output.tar.gz)

Aeronet data as text files (MetPVNet_Aeronet_Input_Data.zip)

Measured data from the MetPVNet measurement campaigns as text files (MetPVNet_Messkampagne_2018(9).tar.gz)

PV power data

Horizontal and tilted irradiance from pyranometers

Longwave irradiance from pyrgeometer

MYSTIC-based lookup table for translated tilted to horizontal irradiance (gti2ghi_lut_v1.nc)

Output data:

Global tilted irradiance (GTI) inferred from PV power plants (with calibration parameters in comments)

Linear temperature model: MetPVNet_gti_cf_inversion_results_linear.tar.gz

Faiman non-linear temperature model: MetPVNet_gti_cf_inversion_results_faiman.tar.gz

Global horizontal irradiance (GHI) inferred from PV power plants

Linear temperature model: MetPVNet_ghi_inversion_results_linear.tar.gz

Faiman non-linear temperature model: MetPVNet_ghi_inversion_results_faiman.tar.gz

Combined GHI averaged to 60 minutes and compared with COSMO data

Linear temperature model: MetPVNet_ghi_inversion_combo_60min_results_linear.tar.gz

Faiman non-linear temperature model: MetPVNet_ghi_inversion_combo_60min_results_faiman.tar.gz

Cloud optical depth inferred from PV power plants

Linear temperature model: MetPVNet_cod_cf_inversion_results_linear.tar.gz

Faiman non-linear temperature model: MetPVNet_cod_cf_inversion_results_faiman.tar.gz

Combined COD averaged to 60 minutes and compared with COSMO and APOLLO_NG data

Linear temperature model: MetPVNet_cod_inversion_combo_60min_results_linear.tar.gz

Faiman non-linear temperature model: MetPVNet_cod_inversion_combo_60min_results_faiman.tar.gz

Validation data:

COSMO cloud optical depth (cosmo_cod_output.tar.gz)

APOLLO_NG cloud optical depth (MetPVNet_apng_extract_all_stations_2018(9).tar.gz)

COSMO irradiance data for validation (cosmo_irradiance_output.tar.gz)

CAMS irradiance data for validation (CAMS_irradiation_detailed_MetPVNet_MK_2018(9).zip)

How to import results:

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):

import pandas as pddata = pd.read_csv("[filename].dat",comment='#',header=[0,1],delimiter=';',index_col=0,parse_dates=True)

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

Note:

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

This dataset contains annotations (i.e. polygons) for solar photovoltaic (PV) objects in the previously published dataset "Classification Training Dataset for Crop Types in Rwanda" published by RTI International (DOI: 10.34911/rdnt.r4p1fr [1]). These polygons are intended to enable the use of this dataset as a machine learning training dataset for solar PV identification in drone imagery. Note that this dataset contains ONLY the solar panel polygon labels and needs to be used with the original RGB UAV imagery “Drone Imagery Classification Training Dataset for Crop Types in Rwanda” (https://mlhub.earth/data/rti_rwanda_crop_type). The original dataset contains UAV imagery (RGB) in .tiff format in six provinces in Rwanda, each with three phases imaged and our solar PV annotation dataset follows the same data structure with province and phase label in each subfolder.Data processing:Please refer to this Github repository for further details: https://github.com/BensonRen/Drone_based_solar_PV_detection. The original dataset is divided into 8000x8000 pixel image tiles and manually labeled with polygons (mainly rectangles) to indicate the presence of solar PV. These polygons are converted into pixel-wise, binary class annotations.Other information:1. The six provinces that UAV imagery came from are: (1) Cyampirita (2) Kabarama (3) Kaberege (4) Kinyaga (5) Ngarama (6) Rwakigarati. These original data collections were staged across 18 phases, each collected a set of imagery from a given Province (each provinces had 3 phases of collection). We have annotated 15 out of 18 phases, with the missing ones being: Kabarama-Phase2, Kaberege-Phase3, and Kinyaga-Phase3 due to data compatibility issues of the unused phases.2. The annotated polygons are transformed into binary maps the size of the image tiles but where each pixel is either 0 or 1. In this case, 0 represents background and 1 represents solar PV pixels. These binary maps are in .png format and each Province/phase set has between 9 and 49 annotation patches. Using the code provided in the above repository, the same image patches can be cropped from the original RGB imagery.3. Solar PV densities vary across the image patches. In total, there were 214 solar PV instances labeled in the 15 phase.Associated publications:“Utilizing geospatial data for assessing energy security: Mapping small solar home systems using unmanned aerial vehicles and deep learning” [https://arxiv.org/abs/2201.05548]This dataset is published under CC-BY-NC-SA-4.0 license. (https://creativecommons.org/licenses/by-nc-sa/4.0/)

This is the accompanying dataset for the publication "Assess Space-Based Solar Power in European-Scale Power System Decarbonization".

The dataset contains the following input data files:

powerplants.csv: power plant-level technical and geographic information used to build generator inputs for the PyPSA-Eur model, including fuel type, capacity, location, efficiency and commissioning year

electricity_demand.csv: hourly electricity demand per country for a full year, used by the PyPSA-Eur model, in MW

europe-2020-era5.nc: hourly weather-based capacity factors for renewable energy technologies per country and technology, derived from ERA5 reanalysis data, used by the PyPSA-Eur model, in per unit (p.u.)

sbsp_rd1(rd2)_profile_2020.nc: hourly normalized generation profiles for RD1 or RD2 SBSP configurations in 2020, representing power output as a fraction of maximum generation, used by the PyPSA-Eur model, in per unit (p.u.)

sbsp_rd1(rd2)_profile_2050.nc: hourly normalized generation profiles for RD1 or RD2 SBSP configurations in 2050, representing power output as a fraction of maximum generation, used by the PyPSA-Eur model, in per unit (p.u.)

costs.csv: cost assumptions, used by the PyPSA-Eur model, units defined in units column

resources/: geospatial and technology-specific input data used by the PyPSA-Eur model, including regional boundaries, renewable generation profiles, spatial constraints, and power plant reference data

The dataset contains the intermediate output data files:

elec_s_37_ec_lcopt_Co2L0.7-3H.nc: PyPSA-Eur network for the year 2020 generated before integrating SBSP, containing techno-economic model outputs including capacities, flows, and costs

elec_s_37_ec_lcopt_Co2L0.0-3H_maximum.nc: PyPSA-Eur network for the year 2050 assuming maximum projected technology costs for all generation technologies

elec_s_37_ec_lcopt_Co2L0.0-3H_minimum.nc: PyPSA-Eur network for the year 2050 assuming minimum projected technology costs for all generation technologies

elec_s_37_ec_lcopt_Co2L0.0-3H.nc: PyPSA-Eur network for the year 2050 assuming average projected technology costs for all generation technologies

The dataset contains the result data files from PyPSA-Eur model runs with integrated SBSP under various scenario settings. Each folder corresponds to a specific combination of scenario year (e.g. 2020, 2050) and SBSP capital cost (in EUR/MW). All results reflect optimized power system configurations with SBSP included.

The following files are provided as examples from the "2050_RD1_267869" scenario, which represents the year 2050 with RD1 (SBSP) assumed to have a capital cost of 267869 EUR/MW:

optimized_2050_rd1_267869_network.nc: Optimized PyPSA-Eur network for 2050 with integrated RD1

2050_middle_rd1_hourly_energy_supply.csv: Hourly energy supply by technology in the optimized network, in MW

2050_middle_rd1_optimization_output.txt: Full solver output from the optimization process

2050_middle_rd1_statistics_cleaned.csv: Aggregated statistics of key components in the network

active_rd1_SBSP_buses.txt: List of buses (nodes) where SBSP is actively installed

generators_2050_rd1_267869.csv: Detailed parameters of all generators in the optimized network

storage_units_2050_rd1_267869.csv: Detailed parameters of all storage units

stores_2050_rd1_267869.csv: Detailed parameters of all stores

sbsp_rd1_p_nom_opt_results.csv: Optimized SBSP installed capacity per node, in MW

Each folder follows the same structure, with file names indicating the year and SBSP capital cost. These results support analysis of SBSP deployment under varying techno-economic assumptions.

Comprehensive dataset containing 159 verified Solar energy equipment supplier businesses in North Carolina, United States with complete contact information, ratings, reviews, and location data.

WW Solar Panel

## Overview

WW Solar Panel is a dataset for object detection tasks - it contains Solar Panel annotations for 1,663 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [BY-NC-SA 4.0 license](https://creativecommons.org/licenses/BY-NC-SA 4.0).

Result data from "Tröndle et al (2025): Socially preferable and technically feasible: European citizens choose solar power and import independence over lower costs".

This repository contains inference results of our main statistical choice model. In addition, it includes choice predictions for all scenarios. All data are within netCDF files (*.nc) and can be opened for example with ArviZ. For the original choice data used in the models, see section "Related works" below.

• choice-model-inference.nc: Inference results of the multi-level choice model.
• scenario-choice-techmix.nc: Choice predictions for scenarios of the technology mix study.
• scenario-choice-sufficiency.nc: Choice predictions for scenarios of the self-sufficiency study.
• trace-plot.png: Trace plot of the main parameters of the multi-level choice model.

If you want to reproduce these data, see workflow cited in the "Related works" section below.

If you use this dataset in an academic publication, please cite the following article:

Tröndle, T., Mey, F., & Lilliestam, J. (2025). Socially preferable and technically feasible: European citizens choose solar power and import independence over lower costs. Energy Research & Social Science, 129, 104364.

Context

After owning my solar panels for a year, I decided to analyze how they were performing and how local weather affected their power generation. This data was part of a large report and analysis that can be found here: https://public.tableau.com/app/profile/tyler.hartshorn/viz/ResidentialSolarReviewMicroandMacroAnalysis/TitlePage

Content

The two datasets combine daily power generation and weather conditions with monthly totals by panel.

Acknowledgements

Thanks to my wife for her ongoing support and my mentor, Gabriel Zhou.

Inspiration

I want home owners to know that residential solar investment can actually be financially lucrative and is not just a quaint home addition.

PyPSA-Eur is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at https://github.com/PyPSA/PyPSA-eur.

It contains alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.

Not all data dependencies are shipped with the code repository, since git is not suited for handling large changing files. Instead we provide separate data bundles to be downloaded and extracted as noted in the documentation.

This is the full data bundle to be used for rigorous research. It includes large bathymetry and natural protection area datasets.

While the code in PyPSA-Eur is released as free software under the MIT, different licenses and terms of use apply to the various input data, which are summarised below:

corine/*

CORINE Land Cover (CLC) database

Source: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/

Terms of Use: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata

natura/*

Natura 2000 natural protection areas

Source: https://www.eea.europa.eu/data-and-maps/data/natura-10

Terms of Use: https://www.eea.europa.eu/data-and-maps/data/natura-10#tab-metadata

gebco/GEBCO_2014_2D.nc

GEBCO bathymetric dataset

Source: https://www.gebco.net/data_and_products/gridded_bathymetry_data/version_20141103/

Terms of Use: https://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_2014_historic.pdf

je-e-21.03.02.xls

Population and GDP data for Swiss Cantons

Source: https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html

Terms of Use:

https://www.bfs.admin.ch/bfs/en/home/fso/swiss-federal-statistical-office/terms-of-use.html

https://www.bfs.admin.ch/bfs/de/home/bfs/oeffentliche-statistik/copyright.html

nama_10r_3popgdp.tsv.gz

Population by NUTS3 region

Source: http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3popgdp&lang=en

Terms of Use:

https://ec.europa.eu/eurostat/about/policies/copyright

GDP_per_capita_PPP_1990_2015_v2.nc

Gross Domestic Product per capita (PPP) from years 1999 to 2015

Rectangular cutout for European countries in PyPSA-Eur, including a 10 km buffer

Kummu et al. "Data from: Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015"

Source: https://doi.org/10.1038/sdata.2018.4 and associated dataset https://doi.org/10.1038/sdata.2018.4

ppp_2019_1km_Aggregated.tif

The spatial distribution of population in 2020: Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

Rectangular cutout for non-NUTS3 countries in PyPSA-Eur, i.e. MD and UA, including a 10 km buffer

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00647

Source: https://data.humdata.org/dataset/worldpop-population-counts-for-world and https://hub.worldpop.org/geodata/summary?id=24777

License: Creative Commons Attribution 4.0 International Licens

data/bundle/era5-HDD-per-country.csv

• Link: https://gist.github.com/fneum/d99e24e19da423038fd55fe3a4ddf875- License: CC-BY 4.0- Contains country-level heating degree days in Europe for 1941-2023. Used for rescaling heat demand in weather years not covered by energy balance statistics.

data/bundle/era5-runoff-per-country.csv

• Link: https://gist.github.com/fneum/d99e24e19da423038fd55fe3a4ddf875- License: CC-BY 4.0- Contains country-level daily sum of runoff in Europe for 1941-2023. Used for rescaling hydro-electricity availability in weather years not covered by EIA hydro-generation statistics.

shipdensity_global.zip

Global Shipping Traffic Density

Creative Commons Attribution 4.0

https://datacatalog.worldbank.org/search/dataset/0037580/Global-Shipping-Traffic-Density

seawater_temperature.nc

Global Ocean Physics Reanalysis

Seawater temperature at 5m depth

Link: https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/services

License: https://marine.copernicus.eu/user-corner/service-commitments-and-licence

Dataset

This dataset was created by Afroz

Released under CC BY-NC-SA 4.0

Contents

This is the model simulation data for paper by Yan Li, Eugenia Kalnay, Safa Motesharrei, Jorge Rivas, Fred Kucharski, Daniel Kirk-Davidoff, Eviatar Bach, and Ning Zeng "Climate model shows large-scale wind and solar farms in the Sahara increase rain and vegetation" in Science, 2018. DOI: 10.1126/science.aar5629File description:1. The "experiment_diff" folderThis folder includes data for the climate impacts of different wind and solar farms experiments. The impacts are the differences in climate averaged from 1951 to 2010. These experiments include:ExpWind0: impacts of wind farm in the SaharaExpSolar0: impacts of solar farm in the SaharaExpWindSolar0: impacts of wind and solar farms in the SaharaExpWindG: impacts of wind farm in the global desert ExpSolarG: impacts of solar farm in the global desert ExpWindSolarG: impacts of wind and solar farms in the global desert ExpWind1: impacts of wind farm in the northwest quarter of the SaharaExpWind2: impacts of wind farm in the northeast quarter of the SaharaExpWind3: impacts of wind farm in the southeast quarter of the SaharaExpWind4: impacts of wind farm in the southwest quarter of the SaharaExpWindmosaic0: impacts of checkerboard A wind farm in the SaharaExpWindmosaic1: impacts of checkerboard B wind farm in the Saharawmask.nc: wind or solar farm locations in the Sahara wmask_g.nc: wind or solar farm location in the global desertContact: Yan Li, yanli.geo@gmail.com

Energy Climate dataset consistent with ENTSO-E Pan-European Climatic Database (PECD 2021.3) in CSV and netCDF format

TL;DR: this is a tidy and friendly version of a recreation of ENTSO-E's PECD 2021.3 data by using ERA5: hourly capacity factors for wind onshore, offshore, solar PV and hourly electricity demand are provided. All the data is provided for 28-71 climatic years (1950-2020 for wind and solar, 1982-2010 for demand).

Description
Country averages of energy-climate variables generated using the Python scripts, based on the ENTSO-E's TYNDP 2020 study. For the following scenario's data is available

• National trends 2025 (NT 2025)
• National trends 2030 (NT 2030)
• National trends 2040 (NT 2040)
• Distributed Energy 2030 (DE 2030)
• Distributed Energy 2040 (DE 2040)
• Global Ambitions (GA 2030)
• Global Ambitions (GA 2040)

The time-series are at hourly resolution and the included variables are:

• Generation wind offshore (aggregated for all years per scenario in a .zip)
• Generation wind onshore (aggregated for all years per scenario in a .zip)
• Generation solar photovoltaic (aggregated for all years per scenario in a .zip)
• Total energy demand (all zones combined in single file per scenario)

The Files are provided in CSV (.csv) & NetCDF (.nc). The data is given per ENTSO-E's bidding zone as used within the TYNDP2020.

DISCLAIMER: the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies.

Yolov8

## Overview

Yolov8 is a dataset for object detection tasks - it contains Bird Drop annotations for 1,126 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [BY-NC-SA 4.0 license](https://creativecommons.org/licenses/BY-NC-SA 4.0).