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210 results for “Energy modeling”
Replication package for the paper "Modeling Europe's role in the global LNG market 2040: balancing decarbonization goals, energy security, and geopolitical tensions"
<p>This package contains folders and files with code and data used in the study described in the paper. Further information can be found on <a href="https://github.com/sebastianzwickl/lng-trade-europe" target="_blank" rel="noopener">GitHub</a>.</p>
AWESOME Energy System Model data and model
<p><span>This repository collects all the necessary items defined to setup and to run the Energy System optimization model for the AWESOME project.</span></p> <p><span>Detailed specifications of the adopted model (OSeMOSYS) and data are descripted in Deliverable D2.4 document: "</span><span>Future Energy Scenarios".</span></p> <p><span>In particular, the repository provides all essential data and scripts to define the energy model defined for projecting the energy scenarios developed for the AWESOME project. The document reports the development of an open-source energy system optimization model of the energy supply chain for the spatial domain useful for the AWESOME project (i.e. including Egypt, Ethiopia, and Sudan). The model is then used to explore different pathways of future energy scenarios in terms of energy demand and infrastructure evolution and their economic and environmental impacts. The future sectoral energy demand scenarios are developed based on the Socio-economic Pathways (SSPs) and the outcomes of D2.1 (Demographic projections), using a multi-sectoral optimal resource allocation economic model. </span></p> <p>This record contains:</p> <p>- The Deliverable D2.4, where the optimization model and the calculation of the energy system scenarios under different climatic scenarios are presented.</p> <p>- The results for each implemented scenario, in terms of installed capacity and energy generation of energy technologies (.tif files and excel files), for the Nile River Basin and at the national level for each focus country (Ethiopia, Sudan and Egypt).</p> <p>- Description of the data (excel file and pdf file)</p>
Modelling and Comparing Converter Architectures and Energy Harvesting ICs for Battery-Free Systems
<p>Artifacts containing measurement scripts, data sets, and simulations for the paper "Modelling and Comparing Converter Architectures and Energy Harvesting ICs for Battery-Free Systems" (currently submitted and under review).</p>
Disentangling Sources of Uncertainty in CLM5 Model Predictions: Water, Energy, and Carbon Fluxes at European Observation Sites
<p>The datasets include:</p> <ul> <li>EC data from Europement measurement sites in <a href="https://www.icos-cp.eu/data-products/2G60-ZHAK">ICOS</a>, <a href="https://fluxnet.org/login/?redirect_to=/data/download-data/">FLUXNETS</a>, and <a href="https://doi.org/10.34731/x9s3-Kr48">COSMOS-Europe</a>.</li> <li>Ensemble simulation data used for analysis</li> </ul> <p>The atmospheric forcings used in driving the model were all local measurements pre-processed using the script in GitHub repository <a href="https://github.com/FedoAIworld/CLM5-Disentangling-Uncertainty/tree/main/00_create_forcing_ds">CLM5-Disentangling-Uncertainty</a>.</p>
Dataset for Learning in Continuous Action Space for Developing High Dimensional Potential Energy Models
<p>The NN potentials developed in this study and the other available MLIP methods such as GAP, SNAP, qSNAP, and MEGNET used for benchmarking.</p>
Data for "Modeling the short-term fire effects on vegetation dynamics and surface energy in southern Africa"
<p>This is the data used for "Modeling the short-term fire effects on vegetation dynamics and surface energy in southern Africa using the improved SSiB4/TRIFFID-Fire model". The data includes two folders: fireon and fireoff representing the scenarios with the fire model turned on and off. Each folder includes 14 years of data from 2000-2013.</p>
Data Bundle for PyPSA-Eur-Sec: A Sector-Coupled Open Optimisation Model of the European Energy System
<p>While small data files used in PyPSA-Eur-Sec are included directly in the git repository, larger ones are collected in this data bundle. The data bundle’s size is around 680 MB.</p> <p><strong>Licenses</strong></p> <p>Different licenses apply to the various components of this data bundle (mostly attribution).</p> <p>For details see <a href="https://pypsa-eur-sec.readthedocs.io/en/latest/installation.html#data-requirements">https://pypsa-eur-sec.readthedocs.io/en/latest/installation.html#data-requirements</a></p> <p><strong>Changelog 0.3.1</strong></p> <ul> <li>Fix IRENASTAT encoding</li> </ul> <p><strong>Changelog 0.3.0</strong></p> <ul> <li>Add <a href="https://pxweb.irena.org/pxweb/en/IRENASTAT">IRENASTAT</a> country-level power generation capacities.</li> </ul> <p><strong>Changelog 0.2.0</strong></p> <ul> <li>add hydrogen salt cavern storage potential (h2_salt_caverns_GWh_per_sqkm.geojson)</li> </ul> <p> </p>
Modelled data - Energy Transition in Bolivia
<p>Model structure, input data and output files from MoManI </p>
Diurnal rainfall response to the physiological and radiative effects of CO2 in tropical forests in the Energy Exascale Earth System Model v1
<p>Necessary outputs and scripts for recreating the figures for the journal article with the same title.</p>
Data bundle for egon-data: A transparent and reproducible data processing pipeline for energy system modeling
<p><strong>egon-data</strong> provides a transparent and reproducible open data based data processing pipeline for generating data models suitable for energy system modeling. The data is customized for the requirements of the research project <strong>eGo<sup>n</sup></strong>. The research project aims to develop tools for an open and cross-sectoral planning of transmission and distribution grids. For further information please visit the eGo<sup>n</sup> <a href="https://ego-n.org/">project website</a> or its <a href="https://github.com/openego/eGon-data">Github repository.</a></p> <p>egon-data retrieves and processes data from several different external input sources. As not all data dependencies can be downloaded automatically from external sources we provide a data bundle to be downloaded by egon-data.</p> <p>The following data sets are part of the available data bundle:</p> <ol> <li><strong>climate_zones_germany</strong> <ul> <li>Climate zones in Germany</li> <li>source: Own representation based on DWD TRY climate zones</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>emobility</strong> <ul> <li>Data on eMobility mit_trip_data:<br> motorized individual travel - individual trips of electric vehicles (EV) generated with a modified version of simBEV v0.1.3 (https://github.com/rl-institut/simbev/tree/1f87c716d14ccc4a658b8d2b01fd12b88a4334d5). simBEV generates driving profiles for BEVs and PHEVs based upon MID data (BMVI) per RegioStaR7 region type (BBSR).</li> <li>Reiner Lemoine Institut, June 2022</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>geothermal_potential</strong> <ul> <li>Spatial distribution of deep geothermal potentials in Germany</li> <li>source: <a href="https://doi.org/10.3390/en11020332">Assessment and Public Reporting of Geothermal Resources in Germany: Review and Outlook</a></li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>household_electricity_demand_profiles</strong> <ul> <li>Annual profiles in hourly resolution of electricity demand of private households for different household types (singles, couples, other) with varying number of elderly and children.<br> The profiles were created using a bottom-up load profile generator by Fraunhofer IEE developed in the Bachelor's thesis "Auswirkungen verschiedener Haushaltslastprofile auf PV-Batterie-Systeme" by Jonas Haack, Fachhochschule Flensburg, December 2012.<br> The columns are named as follows: "<HH_TYPE_PREFIX>a<PROFILE_ID>", e.g. P2a0000 is the first profile of a couple's household with 2 children. See publication below for the list of prefixes. Values are given in Wh.<br> A related conference paper can be obtained here: http://publica.fraunhofer.de/documents/N-374761.html</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>household_heat_demand_profiles</strong> <ul> <li>Sample heat time series including hot water and space heating for single- and multi-familiy houses. The profiles were created using the loadprofile generator by Fraunhofer IEE developed in the Master's thesis "Synthesis of a heat and electrical load profile for single and multi-family houses used for subsequent performance tests of a multi-component energy system", Simon Ruben Drauz, RWTH Aachen University, March 2016</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>hydrogen_storage_potential_saltstructures</strong> <ul> <li>The data are taken from figure 7.1 in Donadei, S., et al., (2020), p. 7-5..</li> <li>Source: Flach lagernde Salze, (c) BGR Hannover, 2021.<br> Datenquelle: InSpEE-Salzstrukturen, (c) BGR, Hannover, 2015. &<br> Donadei, S., Horváth, B., Horváth, P.-L., Keppliner, J., Schneider, G.-S., &<br> Zander-Schiebenhöfer, D. (2020). Teilprojekt Bewertungskriterien und<br> Potenzialabschätzung. BGR. Informationssystem Salz: Planungsgrundlagen,<br> Auswahlkriterien und Potenzialabschätzung für die Errichtung von Salzkavernen<br> zur Speicherung von Erneuerbaren Energien (Wasserstoff und Druckluft) –<br> Doppelsalinare und flach lagernde Salzschichten: InSpEE-DS. Sachbericht.<br> Hannover: BGR.</li> <li>License: The original data are licensed under the GeoNutzV, see https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf</li> </ul> </li> <li><strong>industrial_sites</strong> <ul> <li>Information about industrial sites with DSM-potential in Germany from a Master's thesis by Danielle Schmidt. The data set includes own information on the coordinates of every industrial site.</li> <li>source: Schmidt, Danielle. (2019). Supplementary material to the masters thesis: NUTS-3 Regionalization of Industrial Load Shifting Potential in Germany using a Time-Resolved Model [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3613767</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>nep2035_version2021</strong> <ul> <li>Data extracted from the German grid development plan - power</li> <li>source: Netzentwicklungsplan Strom 2035 (2021), erster Entwurf | Übertragungsnetzbetreiber (M) CC-BY-4.0</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>pipeline_classification_gas</strong> <ul> <li>Parameters for the classification of gas pipelines</li> <li>source: Single parameters extracted from <a href="https://www.econstor.eu/bitstream/10419/173388/1/1011162628.pdf">Electricity, Heat and Gas Sector Data for Modelling the German System</a></li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>pypsa_eur_sec</strong> <ul> <li>Preliminary results from scenario generator pypsa-eur-sec</li> <li>source: own calculation using pypsa-eur-sec fork (https://github.com/openego/pypsa-eur-sec)</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>regions_dynamic_line_rating</strong> <ul> <li>German regions suitable to model dynamic line rating</li> <li>source: Own representation based on <a href="https://www.transnetbw.de/files/pdf/netzentwicklung/netzplanungsgrundsaetze/UENB_PlGrS_Juli2020.pdf">Grundsätze für die Ausbauplanung des Deutschen Übertragungsnetze (2020)</a></li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>re_potential_areas</strong> <ul> <li>Eligible areas for wind turbines and ground-mounted PV systems.</li> <li>Reiner Lemoine Institut, January 2022</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>WZ_definition</strong> <ul> <li>Definitions of industrial and commercial branches</li> <li>source: <a href="https://www.destatis.de/static/DE/dokumente/klassifikation-wz-2008-3100100089004.pdf">Klassifikation der Wirtschaftszweige (WZ 2008)</a></li> <li>Extract from Terms of Use: © Statistisches Bundesamt, Wiesbaden 2008 Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellenangabe gestattet.</li> </ul> </li> <li><strong>zensus_households</strong> <ul> <li>Dataset describing the amount of people living by a certain types of family-types, age-classes,sex and size of household in Germany in state-resolution.</li> <li>source: Data retrieved from <a href="https://ergebnisse2011.zensus2022.de/datenbank/online">Zensus Datenbank</a> by performing these steps: <ul> <li>Search for: "1000A-2029"</li> <li>or choose topic: "Bevölkerung kompakt"</li> <li>Choose table code: "1000A-2029" with title "Personen: Alter (11 Altersklassen)/Geschlecht/Größe desprivaten Haushalts - Typ des privaten Haushalts (nach Familien/Lebensform)"</li> <li>Change setting "GEOLK1" to "Bundesländer (16)" higher resolution "Landkreise und kreisfreie Städte (412)" only accessible after registration.</li> </ul> </li> <li>Extract from Terms of Use: © Statistische Ämter des Bundes und der Länder 2021, Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellennachweis gestattet.</li> </ul> </li> </ol> <p> </p>
Data set containing the energy landscapes for GPO and GPP tropocollagen models under pulling forces
<p>Energy landscapes (databases of minima and transition states) for GPO and GPP repeat collagen models under constant pulling forces as explored with OPTIM and PATHSAMPLE with an AMBER force field.</p> <p>The systems are seven GPO or GPP per chain capped with ACE and NME.</p> <p> The forces applied are 0 pN (F0), 10 pN (F1), 50 pN (F2), 100 pN (F3), 250 pN (F4), 500 pN (F5) and 750 pN (F6).</p> <p>The folders contains numerous analysis scripts and graphs. Most of these assume python with numpy and pandas, as well as cpptraj from AMBERTools.</p>
European power system infrastructure in the open energy system model PyPSA-Eur
<p>The image is created using the data and scripts in the European open energy system model <a href="https://github.com/PyPSA/pypsa-eur">PyPSA-Eur.</a></p>
Deposition of amorphous carbon at different energies modeled with GAP
<p>These videos show the simulated deposition, one atom at a time, of amorphous carbon (a-C) films. The system is equilibrated to 300 K after each impact and before the next deposition event. Different deposition energies are simulated, from 1 eV to 100 eV. The atoms are deposited onto a preexisting diamond substrate, shown in the 60 eV video; after 2500 depositions at 60 eV, the generated a-C film is used as template to deposit all the other films. The interatomic interactions are modeled with the a-C GAP of Deringer and Csányi [Phys. Rev. B <strong>95</strong>, 094203 (2017)] interfaced through LAMMPS [http://lammps.sandia.gov]. The visualization is carried out with VMD [http://www.ks.uiuc.edu/Research/vmd] using Axel Kohlmeyer's TopoTools [DOI: 10.5281/zenodo.545655]. For further information, refer to Phys. Rev. Lett. <strong>120</strong>, 166101 (2018) and Phys. Rev. B <strong>102</strong>, 174201 (2020). Funding from the Academy of Finland (grants 310574 and 285526) and computational resources from CSC [http://www.csc.fi] are acknowledged.</p>
Model data for " Topography Influence on Changes in Spatial Distribution of Eddy Kinetic Energy in the Southern Ocean"
<p>This dataset is for the paper " Topography influence on Changes in Spatial Distribution of Eddy Kinetic Energy in the Southern Ocean"</p>
Finite element analysis results from simulation of fusion energy heat exchange component: hybrid CAD/IBSim model including a graphite foam interlayer
<p>Temperature profile data from a finite element analysis of a conceptual design for a fusion energy heat exchange component (monoblock). The mesh is a hybrid from a computer aided design (CAD) drawing for the pipe and armour and IBSim for the interlayer. The IBSim interlayer is generated directly from a 3D volumetric image of a graphite foam block (KFoam). The 3D image was generated with an X-ray tomography scan performed by Dr Llion Evans with Manchester X-ray Imaging Facility equipment, which was funded in part by the EPSRC (grants EP/F007906/1, EP/F001452/1 and EP/I02249X/1). Conversion of the data to FE mesh was achieved using ScanIP, part of the Simpleware suite of programmes, version 7 (Synopsys Inc., Mountain View, CA, USA).</p> <p>The mesh used for the analysis is available as a separate dataset:</p> <p><a href="https://doi.org/10.5281/zenodo.3522319">https://doi.org/10.5281/zenodo.3522319</a></p> <p>This data was used originally for the following publications (please cite if re-using the data):</p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. <a href="https://doi.org/10.1016/j.carbon.2018.10.031">https://doi.org/10.1016/j.carbon.2018.10.031</a></p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Improving modelling of complex geometries in novel materials using 3D imaging”, Proceedings of NEA International Workshop on Structural Materials for Innovative Nuclear Systems, Manchester, UK, July 2016. <a href="https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf">https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf</a></p>
Dataset and models from: Covariant Jacobi-Legendre expansion for total energy calculations within the projector-augmented-wave formalism
<p>Scripts, related code, files, and dataset for the paper: Covariant Jacobi-Legendre expansion for total energy calculations within the projector-augmented-wave formalism.</p>
MOVES-Matrix 3.0: On-Road Energy and Emission Modeling with High-Performance Supercomputing
<p><span>This is the dataset for the NCST project "</span><span>MOVES-Matrix 3.0: On-Road Energy and Emission Modeling with High-Performance Supercomputing</span><span>"</span></p> <p> </p> <p><span>The Georgia Tech research team developed MOVES-Matrix 3.0 based on the EPA's MOVES3 (version 3.1.0) energy use and emission rate model by running MOVES3 thousands of times on the PACE supercomputing cluster across all combinations of input variables and storing the output as lookup tables.<span> </span>MOVES-Matrix 3.0 allows on-road energy consumption and emissions modeling to be conducted more than 800 times faster than running MOVES, while it generates the exact same results, as verified in this report. <span> </span>MOVES-Matrix 3.0 was designed similarly to its predecessor, MOVES-Matrix 2014, but required extensive code modifications to accommodate changes in the MOVES3 environment (including a shift from MySQL to MariaDB and incorporation of new vehicle source sub-types and operating parameters). <span> </span>The review of the fuel and I/M scenarios indicated that MOVES3 now defines 122 modeling regions, as compared with 109 regions in MOVES 2014b (different matrices need to be developed each modeling region).<span> </span>The development of matrices for each modeling region takes approximately 15-20 days on the PACE supercomputing cluster given our assigned resources (compared with only 5-7 days to develop matrices for MOVES 2014). <span> </span>A case study of 3,000 roadway links using Atlanta's matrices confirmed that MOVES-Matrix 3.0 produces the exact same energy consumption and emissions results as MOVES3, but execution modules operate 800 times faster using MOVES-Matrix lookups than running MOVES for any single run.</span></p>
The influence coefficients used in Wind Energy Science paper "A computationally efficient engineering aerodynamic model for swept wind turbine blades"
<p>The influence coefficients for the convective correction with full double-precision floating-point accuracy. This is the supplement for the research article: "A computationally efficient engineering aerodynamic model for swept wind turbine blades", submitted to Wind Energy Science journal.</p> <p>Code language: Fortran</p>
Integration of prosumer peer-to-peer trading decisions into energy community modelling
<p>Peer-to-peer (P2P) exchange of renewable energy is an attractive option to empower citizens to actively participate in the energy transition. Whereas previous research has assessed P2P communities primarily from a techno-economic perspective, little is yet known about prosumer preferences for solar power trading. Importantly, the impacts of community members’ trading decisions on key performance indicators such as individual electricity bills, community autarky, and grid stress remain unknown. Here, we assess P2P trading strategies of German homeowners based on an online experimental study. We simulate how various decision-making strategies impact the performance of P2P communities. The findings suggest that community autarky is slightly higher when prosumers are enabled to trade energy compared to when they merely aim to maximize their self-consumption. Our analysis moreover shows that P2P energy trading based on human decision-making may lead to financial benefits for prosumers and traditional consumers, and reduced stress for the grid.</p>
Wood Brook catchment: A coupled phenology – surface energy balance model to understand stream – subsurface temperature dynamics
<p>This folder contains data, model input files, model executable and source code required to reproduce results in the paper:"Evaluating a coupled phenology – surface energy balance model to understand stream – subsurface temperature dynamics in a mixed-use farmland catchment" by Han Qiu, Phillip Blaen, Sophie Comer-Warner, David M. Hannah, Stefan Krause and Mantha S. Phanikumar (Water Resources Research).</p> <p>The catchment (referred to herein as Wood Brook, Mill Brook, Mill Haft, BIFOR) is a mixed-use farmland catchment in central England. The "Data" folder contains information needed to create model input files. The "Figures" folder contains MS excel files with observed data (streamflows, groundwater heads, stream, streambed and groundwater temperatures etc) and simulation results to recreate the figures in the WRR paper. The other three folders contain model inputs, outputs and source code.</p>
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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.