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210 results for “energy modelling”
1600 years of modelled energy production and demand for European Countries (Norway, France, Italy, Spain, and Sweden)
<h3>Citation</h3> <p>When using this dataset, please cite the following paper: van der Most et al. Temporally compounding energy droughts in European electricity systems with hydropower, 10 January 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-3796061/v1].</p> <h3>Description</h3> <p>This dataset contains daily renewable energy production and demand data used in the study "Temporally compounding energy droughts in European electricity systems with hydropower". The dataset includes production data for various renewable energy sources (offshore wind, onshore wind, solar photovoltaics, run-of-river, and hydropower reservoir inflow) and electricity demand. It was generated wit the use of 1600 years of climate model data and a daily renewable electricity production and demand modelling framework. The study focuses on five European countries with significant hydropower capacities: Norway, France, Italy, Spain, and Sweden.</p> <h3>Content</h3> <ul> <li> <p><strong>Energy Production Data</strong>:</p> <ul> <li>Offshore and Onshore Wind Power: Derived from 10 m wind speed data extrapolated to hub height, using power law equations and cubic power curves.</li> <li>Solar Photovoltaics (PV): Based on solar irradiance and temperature-dependent cell efficiency calculations.</li> <li>Hydropower: Includes inflow data for run-of-river and reservoir hydropower systems modelled with routed runoff data</li> <li>Hydropower dispatch is modelled at the national level using a linear optimization approach that aims to minimize the difference between demand and the sum of all renewable energy production over a year, directing the solution to following the load curves.</li> </ul> </li> <li> <p><strong>Energy Demand Data</strong>:</p> <ul> <li>Daily load data from ENTSO-E tranparancy fitted using a logistic smooth transmission regression approach to national mean, population-weighted daily near-surface temperatures from ERA5 reanalysis data.</li> <li>Demand curves account for weekdays and weekends but exclude cultural and socio-economic factors such as holidays.</li> </ul> </li> </ul> <h3>Methodology</h3> <p>The dataset is generated using the KNMI Large Ensemble Time Slice (KNMI-LENTIS) dataset, which includes 160 sets of 10-year physical climate model simulations of present-day climate (2000-2009). The simulations are conducted with the EC-Earth3 global climate model. The energy production and demand data are modeled to assess the impact of meteorological drivers on energy systems, with a focus on identifying periods of high residual loads (energy droughts). The model set-up has been validated with the use of ERA5 data in previous work. </p> <h3>Usage</h3> <p>This dataset is intended for researchers and policymakers interested in studying the impact of climate variability on renewable energy systems. It provides insights into how different meteorological conditions can lead to energy droughts and offers a basis for developing strategies to enhance the resilience of energy systems.</p> <p> </p>
Historical and modelled renewable energy production for India
<p>This archive contains all the datasets produced for the paper:<br><br><span>Hunt, K. M. R.</span>, & <span>Bloomfield, H. C.</span> (<span>2024</span>). <span>Quantifying renewable energy potential and realized capacity in India: Opportunities and challenges</span>. <em>Meteorological Applications</em>, <span>31</span>(<span>3</span>), e2196. <a href="https://doi.org/10.1002/met.2196">https://doi.org/10.1002/met.2196</a></p> <p> </p> <table style="border-collapse: collapse; width: 99.9642%;"><colgroup><col style="width: 31.0476%;"><col style="width: 17.1785%;"><col style="width: 37.7477%;"><col style="width: 14.0133%;"></colgroup> <tbody> <tr> <td><strong>Data Description </strong></td> <td><strong>Figure/Table</strong></td> <td><strong> File Name</strong></td> <td><strong>Dates Valid</strong></td> </tr> <tr> <td>Installed capacity by type in each state</td> <td>Table 1</td> <td>installed-by-state-oct2022.csv</td> <td>Oct 2022</td> </tr> <tr> <td>All-India installed capacity by type</td> <td>Figure 2</td> <td>tabulated-installed-by-date.csv</td> <td>2017–2023</td> </tr> <tr> <td>Hourly wind capacity factor</td> <td>Figure 4</td> <td>wind capacity factor.zip</td> <td>1979–2022</td> </tr> <tr> <td>Hourly solar capacity factor</td> <td>Figure 6</td> <td>solar capacity factor.zip </td> <td>1979–2022</td> </tr> <tr> <td>Present-day installation locations</td> <td>Figure 11</td> <td>OSM[hydropower,wind_turbine,solar]_ installations.geojson</td> <td>Mar 2022</td> </tr> <tr> <td>Gridded 1°×1° estimate of installed wind/solar capacity</td> <td>Figure 12a/13a</td> <td>CEA_1x1_gridded_installed_[wind,solar]_cap.nc</td> <td>May 2021</td> </tr> <tr> <td>Gridded 1°×1° estimate of installed wind capacity</td> <td>Figure 12b</td> <td>TWP_1x1_gridded_installed_wind_cap.nc</td> <td>May 2021</td> </tr> <tr> <td>Gridded 1°×1° estimate of installed solar capacity</td> <td>Figure 13b</td> <td>K21_1x1_gridded installed solar cap.nc</td> <td>Sep 2018</td> </tr> <tr> <td>Reported daily wind/solar/hydro production</td> <td>Figure 14/S3</td> <td>POSOCO_reported_[wind,solar,hydro]_MU_ daily.csv</td> <td>2012–2023</td> </tr> <tr> <td>Modelled ‘historical’ production</td> <td>Figure 14/16/S4a/b</td> <td>modelled-historical-[daily,hourly]-renewable output.nc</td> <td>1979–2022</td> </tr> <tr> <td>Recommended locations for new wind/solar installations</td> <td>Figure 17</td> <td>areas-for-exploration.nc</td> <td>--</td> </tr> </tbody> </table> <p> </p> <p> </p>
EPA Integrated Planning Model (IPM) National Electric Energy Data System (NEEDS) database
EPA is making the latest power sector modeling platform available, including the associated input data and modeling assumptions, outputs, and documentation.
Investigating dynamics between energy use and socio-demographic characteristics in spatial modeling of residential energy consumption
<p>Files represent datasets (2017 Residential Building Stock Assessment and American Community Survey 2012-2017 5-year estimate) and R-code associated with the analysis. </p>
Supplementary material for "Song et al., Modelling Simul. Mater. Sci. Eng., 2021: Data-mining of dislocation microstructures: concepts for coarse-graining of internal energies"
<p>This zip archive contains supplementary material in the form of datasets and jupyter notebooks that are used in the following publication:</p> <ul> <li>authors: Hengxu Song, Nina Gunkelmann, Giacomo Po, and Stefan Sandfeld</li> <li>journal: Modelling Simul. Mater. Sci. Eng.</li> <li>year: 2021</li> <li>title: Data-mining of dislocation microstructures: concepts for coarse-graining of internal energies</li> </ul>
Dataset: The effects of class balance on the training energy consumption of logistic regression models
<p>Two synthetic datasets for binary classification, generated with the Random Radial Basis Function generator from WEKA. They are the same shape and size (104.952 instances, 185 attributes), but the "balanced" dataset has 52,13% of its instances belonging to class c0, while the "unbalanced" one only has 4,04% of its instances belonging to class c0. Therefore, this set of datasets is primarily meant to study how class balance influences the behaviour of a machine learning model.</p>
Data set for the integrated Climate, Land, Energy and Water systems modelling exercise RCLEWs in OSeMOSYS
<p>This dataset refers to the modelling exercise (version01_210616RCLEWs). The dataset contains the OSeMOSYS code used to run the modelling exercise, the model input data, the scenarios model data files, and the results. The code for the results visualization is available at https://github.com/KTH-dESA/teaching-CLEWs_visualization.</p> <p>This is an update of version 01_210827 available at: https://doi.org/10.5281/zenodo.5293834</p>
Ensemble statistics for modelled Eddy Kinetic Energy in the Southern Ocean
<p>This dataset contains surface eddy kinetic energy over the Southern Ocean region, sourced from a 50-member ensemble of 0.25° ocean model simulations. It is used in the paper "Circumpolar variations in the chaotic nature of Southern Ocean eddy dynamics" published in Journal of Geophysical Research - Oceans.</p> <p>This dataset has been computed from the OceaniC Chaos – ImPacts, strUcture, predicTability (OCCIPUT) global ocean/sea-ice ensemble simulation. It is composed of 50 members with a horizontal resolution of 1/4° and 75 geopotential levels (<a href="http://doi.org/10.5194/gmd-10-1091-2017">Bessières et al., 2017</a>, Penduff et al., 2014). The numerical configuration is based on the version 3.5 of the NEMO model (<a href="https://www.nemo-ocean.eu/doc">Madec, 2008</a>). The 50 members were started on January 1st 1960 from a common 21-year spinup. A small stochastic perturbation is applied to the equation of state of sea water (as in <a href="https://doi.org/10.1016/j.ocemod.2013.02.004">Brankart, 2013</a>) within each member during 1960, then switched off during the rest of the simulation. This 1-year perturbation generates an ensemble spread which grows and saturates after a few months up to a few years depending on the region. The 50 members are driven through bulk formulae during the whole 1960-2015 simulation by the same realistic 6-hourly atmospheric forcing (Drakkar Forcing Set DFS5.2, Dussin et al., 2016) derived from ERA interim atmospheric reanalysis. Data is for the period 1979-2015.</p> <p>The sea level anomaly is found according to <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al (2020)</a> and converted into surface geostrophic velocity anomaly using the geostrophic relation. This velocity field is then used to calculate the eddy kinetic energy (EKE). Data is averaged over calendar month, and restricted to the latitude range 40°-60°S. A full description of this process is included in the companion paper.</p> <p>The dataset includes EKE files (eke_0??.nc), with monthy EKE saved for the period 1979-2015 for each ensemble member, and a single file (tau.nc) for the monthly-averaged wind stress over the same period.</p>
Global Environmental and Weather data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.
<p><strong>PyPSA-Earth </strong>is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base.</p> <p>Since the GitHub codebase is not suited for handling large changing files, we provide here separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a></p> <p>The below-provided <strong>cutouts </strong>are spatiotemporal subsets of the Earth weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset and the <a href="https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002">CMSAF SARAH-2</a> solar surface radiation dataset for the <strong>year 2013</strong>. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>). They can be reproduced or extended for other weather years (approx. 40-50 years) around the world by using the <a href="https://github.com/pypsa-meets-africa/pypsa-africa/blob/main/scripts/build_cutout.py">build.cutout.py</a></p> <p><strong>ECMWF ERA5</strong></p> <ul> <li><strong>Source: </strong><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li><strong>Terms of Use: </strong><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul>
Dataset of EnergyPlus models to evaluate the impact of modeling the hysteresis phenomenon of phase change materials on the building energy performance
<p>This dataset is the research data generated to evaluate the impact of modeling the hysteresis phenomenon of phase change materials (PCM) on the building performance simulation, which includes:<br> - A series of EnergyPlus models representing the medium office of the Prototype Building Models developed by DOE. These are the original model without PCM (Baseline), and four models with different PCM modeling approaches (melting-curve, solidification-curve, mean-curve, hysteresis-model).<br> - The typical meteorological year (TMY) for Frankfurt city that was used to obtain the results, which is freely provided by Climate.One.Building.Org repository (https://climate.onebuilding.org/).</p>
Auxiliary Euro-Calliope datasets: QTDIAN storyline-specific spatial data to represent a European energy system model at several spatial resolutions
<p>Custom output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow.</p> <p>This output provides similar data to <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with three additional land area scenarios.</p> <p>These scenarios are in line with three storylines from the <a href="https://zenodo.org/record/5834010">QTDIAN toolbox</a> and are based on updating the `possibility-for-electricity-autarky` workflow configuration to include the following parameters (also included in `config.yaml`):</p> <p> </p> <pre><code> scenarios: people-powered: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 0.2 # agro pv share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 0.1 share-rooftop-used: 1.0 government-directed: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0 market-driven: use-of-protected areas: true pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 1.0 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0</code></pre> <p> </p> <p>This dataset includes different spatial resolutions of land availability. For more information on the `ehighways` resolution, see <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a>.</p> <p>This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>.</p> <p> </p>
Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models
<p>This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models" to be published in the journal Animal - Open Space.</p>
Estimating surface water availability in high mountain rock slopes using a numerical energy balance model
<p>Model output, forcing data and physical parameters used to estimate water and energy balance. The model was calibrated with field measurements from a study site in the Mont-Blanc massif, at 3842 m a.s.l, at a slope of 55 deegrees and aspect azimut of 150 degrees (south-east). The different ModelOutput files are from simulations at different elevastions (from 4800 m to 2700 m at steps of 300 m). We used the CryoGrid community model (version 1.0) toolbox (Westermann et al., 2022) to simulate the 1D ground thermal regime and ice/water balance, and estimate the availability of surface water and its potential for infiltration in rock fractures. The S2M-SAFRAN dataset combines output from a numerical weather prediction model and <em>in situ</em> observations, and was originally developed for operational needs to estimate avalanche hazard in mountainous areas (Durand et al., 1993). The S2M-SAFRAN dataset that we used is available for various mountain areas, at elevation steps of 300 m, and with an hourly resolution between the years 1958 to 2021 (Vernay et al., 2022). It includes most parameters that are required for modeling with CryoGrid: Relative humidity, air T, incoming long wavelength radiation, incoming short wavelength solar radiation, and wind speed. To complete the forcing data we used top of the atmosphere incident solar radiation from ERA5 global reanalysis dataset (Hersbach et al., 2020).</p>
Global monthly discharge dataset, derived from dynamical 1-D water-energy routing model (DynWat) at 10 km spatial resolution
<pre>Global 10km spatial resolution discharge dataset at the global scale for all major rivers, lakes and reservoirs. Data are provided at a monthly temporal resolution.</pre>
Dataset for "An alternative to market-oriented energy models: nexus patterns across hierarchical levels"
<p>Dataset used for the publication "Di Felice, Louisa Jane, Maddalena Ripa, and Mario Giampietro. "An alternative to market-oriented energy models: Nexus patterns across hierarchical levels." <em>Energy Policy</em> 126 (2019): 431-443.". The dataset follows the distinction across hierarchical levels as specified in the publication.</p> <p>The same dataset was also used for a case study developed for the MAGIC project, available <a href="http://magic-nexus.eu/case_study/electric-grid-catalonia-illustrations-musiasem">here</a>. </p>
Global monthly water temperature dataset, derived from dynamical 1-D water-energy routing model (DynWat) at 10 km spatial resolution
<pre>Global 10km spatial resolution water temperature dataset at the global scale for all major rivers, lakes and reservoirs. Data are provided at a monthly temporal resolution.</pre> <p>V1.1 update includes a improved version of the model removing some initial spikes related to rapid ice melt and streams that fall dry. The record has been reduced from 1981 tot 2014 to remove potential spinup impacts.</p> <p>The 1960-2010 data from v1.0 can be used for the earlier years.</p> <p>Consistent forcing is used for both time periods to remove potential biases that might occur otherwise.</p>
Turbulent kinetic energy over large wind farms observed and simulated by the mesoscale model WRF (3.8.1)
<p>This repository contains the WRF configuration files necessary to reproduce the simulations <br> as described in Siedersleben et al. 2019 (https://doi.org/10.5194/gmd-2019-100)</p> <p>The file windturbines_GMD.txt contains the locations of <br> all windturbines implemented in the simulations. The corresponding attributes of each <br> wind turbine type is described in the wind-turbine-xx.tbl. Be aware that all windturbines use the same power and thrust coefficients only the different hub heights and rotor diameters are taken into account as described in Siedersleben et al. (2019).</p> <p>The namelist.input_nameOfSimulation files necessary to run the simulations are provided in this repository as well. You may notice that <br> there are less namelist files than simulations. The simulations not using a TKE source use the same namelists as the ones with a TKE a source. However, the WRF model needs to be recompiled using the manipolated module_wind_fitch.F (you find this file in this repository). The sensitivity studies investigating the impact of the uncertainties in the power and thrust coefficients use the namelist of the control simulation CNTRb, but with manipulated wind-turbine-x_modMin/Max.tbl wind turbine files.</p> <p>The two python files get_era5*.py can be used to retrieve the ERA5 data, driving the WRF model. <br> Note that the dates and pathes have to be adjusted in the python files. <br> After downloading the surface and model level data some postprocessing <br> is necessary as described nicely here: "http://valcap74.blogspot.com/2017/10/how-to-run-wrf-model-driven-by-era5-on.html". For this<br> purpose the simple script called postProcessERA5 (based on the blog entry mentioned above) can be used.</p>
Research Data/Code for "Scale-bridging within a complex model hierarchy for investigation of a metal-fueled circular energy economy by use of Bayesian model calibration with model error quantification"
<p>This repository contains research data and code for supplementing the manuscript <br>"Scale-bridging within a complex model hierarchy for investigation of a metal-fueled circular energy economy by use of Bayesian model calibration with model error quantification" <br>by L. Gossel, E. Corbean, S. Dübal, P. Brand, M. Fricke, H. Nicolai, C. Hasse, S. Hartl, S. Ulbrich, and D. Bothe. </p> <p>There is a corresponding preprint available on Arxiv: https://doi.org/10.48550/arXiv.2404.13092</p> <p><br>Users are referred to the manuscript for background information. This repository shall enable reproduction of the reported results and does not stand alone. </p> <p>Please read important information in the README in the top-level directory. </p> <p>Funded by the Hessian Ministry of Higher Education, Research, Science and the Arts - cluster project Clean Circles. </p>
Open and Lite Techno-economic Dataset for Long-term Energy Systems Modelling in the Republic of South Africa
<p>An open-source lite techno-economic dataset for long term energy systems modelling in the Republic of South Africa. Includes data on electricity generation and demand, electricity imports and exports, power transmission and distribution, residual capacity, capacity factor, operational lifetime, and fixed, variable and capital costs of electricity generation technologies. It also contains estimates for renewable potential and fossil fuel reserves in South Africa.</p>
The International Transport Energy Modeling (iTEM) Open Data & Harmonized Transport Database
<p>This dataset and documentation contains detailed information of the iTEM Open Database, a harmonized transport data set of historical values, 1970 - present. It aims to create transparency through two key features:</p> <ul> <li>Open-Data: Assembling a comprehensive collection of publicly-available transportation data</li> <li>Open-Code: All code and documentation will be publicly accessible and open for modification and extension. <a href="https://github.com/transportenergy">https://github.com/transportenergy</a></li> </ul> <p>The iTEM Open Database is comprised of individual datasets collected from public sources. Each dataset is downloaded, cleaned, and harmonised to the common region and technology definitions defined by the iTEM consortium https://transportenergy.org. For each dataset, we describe the name of the dataset, the web link to the original source, the web link to the cleaning script (in python), variables, and explain the data cleaning steps (which explains the data cleaning script in plain English).</p> <p>Shall you find any problems with the dataset, please report the issues here <a href="https://github.com/transportenergy/database/issues">https://github.com/transportenergy/database/issues</a>. </p> <p> </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.