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210 results for “Energy modeling”
An approach using performance models for supporting energy analysis of software systems
<p>Replication package of the paper titled "An approach using performance models for supporting energy analysis of software systems". Usage instructions are contained in the README.md file.</p>
Results of linear ocean model experiment for: Dual wave energy sources for the Atlantic Niño events identified by wave energy flux in case studies
<p>These are sensitivity experiments designed by manipulating the wind forcing that drives the linear ocean model to investigate the difference in equatorial waves in 1999, 2019, and 2021. </p>
Urban Building Energy Modelling for the Renovation Wave: A Bespoke Approach Based on EPC Databases
<p>Dataset associated to the article: Rodríguez-Álvarez, J.Urban Building Energy Modelling for the Renovation Wave: A Bespoke Approach Based on EPC Databases. <em>Buildings </em><strong>2023</strong>, <em>13</em>, x.</p> <p>It contains filtered EPC datasets as xls and csv and shapefiles with the buildings' geometry and estimated energy loads</p>
Dataset of paper "Removal of diclofenac by UV-B and UV-C light-emitting diodes (LEDs) driven advanced oxidation processes (AOPs): Wavelength dependence, kinetic modelling and energy consumption"
<p>Dataset of paper "Removal of diclofenac by UV-B and UV-C light-emitting diodes (LEDs) driven advanced oxidation processes (AOPs): Wavelength dependence, kinetic modelling and energy consumption"</p> <ul> <li>Molar absorption coefficient of the DCF (pH 7.2), FC (pH 8.5), and H<sub>2</sub>O<sub>2</sub> (pH 6.5) in the wavelength range of 200-400 nm. </li> <li>Time-based and UV fluence-based kinetic constant and synergy factor for the DCF degradation.</li> <li>Diclofenac degradation fitted by the proposed models (UV/H<sub>2</sub>O<sub>2</sub> and UV/FC).</li> <li>Oxidant degradation fitted by the proposed models (UV/H<sub>2</sub>O<sub>2</sub> and UV/FC).</li> </ul>
Modelling results for the paper "The impact of methane leakage on the role of natural gas in the European energy transition"
<p>This data file represents the modelling results of the paper "The impact of methane leakage on the role of natural gas in the European energy transition" that is accepted to Nature Communications.</p>
Simulation data for the office cell building energy model with the attached overhang
<p>Simulation data for 729,000 variants of the office cell building model with the overhang attached over the window. The variants are determined by the overhang depth and height, location, presence of obstacles, orientation and cooling and heating set points. The office cell model is described in the manuscript "Predicting the shape of loads for an office cell with an overhang from a small number of building energy simulations".</p>
Data for A Physical Model for the Observed Inverse Energy Cascade in Typhoon Boundary Layers
<p>This repository contains dataset for the paper entitled "A Physical Model for the Observed Inverse Energy Cascade in Typhoon Boundary Layers". The magnitude of inverse energy cascade flux is revised in version 2.0 according to Xia et al. (2009) (https://doi.org/10.1063/1.3275861). </p>
Data used for modeling in Energy-water-land-CCUS nexus model: carbon dioxide opportunities based on optimized regional development
<p>In this dataset, the data used for modeling technologies in an energy-water-land-CCUS nexus model in Khark Island in Iran, and the main sources for gathering them are presented.</p>
Code: Decarbonization Employment and Energy Systems (DEERS) Model
<p>The Decarbonization Employment and Energy Systems (DEERS) model is a data-driven framework for estimating labor market pathways of large-scale, low-carbon energy-supply infrastructure development. The DEERS model is designed as a tool to inform regional and national workforce and infrastructure planning and policy-making in the U.S. The model simulates the distribution of labor effects over time and across economic sectors, resource sectors, occupations, and geography for multi-decadal energy-supply system transition scenarios. The model is used to estimate employment demand and wages, as well as experience, education, and training requirements, across domestic energy supply chains. We also incorporate time-variant factors, such as labor productivity and wage inflation, which are especially important in the context of emerging labor markets and long-term transitions. The DEERS model is adaptable to different energy system contexts and readily coupled with regional and downscaled macro-energy system modeling outputs. It can also be used to explore modifiable workforce and infrastructure planning and policy decisions, such as high road labor policies, siting domestic manufacturing facilities, creating just transition funds, and changing fossil fuel exports over time.</p>
Dataset and description of an EnergyPLAN model of the Italian energy system in 2021
<p>This document describes an EnergyPLAN model of the Italian energy systems for the year 2021.</p> <p>The dataset, consisting of the EnergyPLAN input files necessary to run the simulation, is also provided.</p> <p>See the EnergyPlan website (<a href="https://www.energyplan.eu/">https://www.energyplan.eu/</a>) for instructions.</p>
Supporting data for "Measuring the Loschmidt amplitude for finite-energy properties of the Fermi-Hubbard model on an ion-trap quantum computer"
<p>This repository contains the supporting data for the publication: "Measuring the Loschmidt amplitude for finite-energy properties of the Fermi-Hubbard model on an ion-trap quantum computer".</p>
Case study result data set for the submitted article "Implications of hydrogen import prices for the German energy system in a model-comparison experiment"
<p>The data set contains result data for the German energy system in a long term scenario (scenario year 2045) as described in the publication "Implications of hydrogen import prices for the German energy system in a model-comparison experiment". The results have been generated with the models REMod of Fraunhofer Institute for Solar Energy Systems ISE, Enertile of Fraunhofer Institute for Systems and Innovation Research ISI, and SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE.</p><p><strong>Abbreviations:</strong></p><ul><li>BEV - Battery Electric Vehicles</li><li>CC - Combined Cycle</li><li>CCGT - Combined Cycle Gas Turbine</li><li>CHP - Combined heat and power</li><li>CO2 - Carbon dioxide</li><li>con - consumption</li><li>FC - Fuel Cell</li><li>FCEV - Fuel Cell Electric Vehicle</li><li>gen - generation</li><li>H2 - Hydrogen</li><li>HT - High temperature</li><li>ICE - Internal Combustion Engine</li><li>LDV - Light-Duty Vehicle</li><li>LT - Low temperature</li><li>med - medium</li><li>OC - Open Cycle</li><li>OCGT - Open Cycle Gas Turbine</li><li>PHEV - Plug-In Hybrid Vehicles</li><li>PS - Pumped Storage</li><li>PV - Photovoltaics</li><li>ST - Steam turbine</li><li>SynFuel - Synthetic fuel</li><li>w/ - with</li><li>w/o - without</li><li>yr - year</li></ul>
Results of linear ocean model experiment for: Dual wave energy sources for the Atlantic Niño events identified by wave energy flux in case studies
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A predictive flight-altitude model for avoiding future conflicts between an emblematic raptor and wind energy development in the Swiss Alps
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Data for: Accurate sequence-to-affinity models for SH2 domains from multi-round peptide binding assays coupled with free-energy regression
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Comparing power-system- and user-oriented battery electric vehicle charging representation and its implications on energy system modeling
<p>This supplementary material includes data and code for the research described in the paper "Comparing power-system- and user-oriented battery electric vehicle charging representation and its implications on energy system modeling". The code containts an interface between the output files of the agent-based simulation model CURRENT and the energy system optimization model REMix as well as some scripts for analyzing REMix results. The data folder contains input data for REMix, the complete list of all model runs analyzed in the paper in the GAMS format .gdx as well as Excel files containing annual results of the sensitivity runs and respective pivot tables and figures for respective analysis.</p>
Dataset for Metabolic Cost Calculations of Gait using Musculoskeletal Energy Models, a Comparison Study
<p>This data set contains raw and processed data of gait analysis experiments of level and inclined walking at two speeds for 12 participants. The slopes were uphill and downhill with 8% incline. The raw data contains the output of the force plates and marker data, as well as raw measurements from an K4B2 system. Mat files are processed data: measured metabolic rate, and measured and calculated metabolic cost, as well as kinetic and kinematic data of an averaged gait cycle: joint angles, velocities and moments, ground reaction forces, muscle activation, contractile element length and stimulation, and the duration of the gait cycle.</p>
Data from: Energy benefits and emergent space use patterns of an empirically parameterized model of memory-based patch selection
Many species frequently return to previously visited foraging sites. This bias towards familiar areas suggests that remembering information from past experience is beneficial. Such a memory-based foraging strategy has also been hypothesized to give rise to restricted space use (i.e. a home range). Nonetheless, the benefits of empirically derived memory-based foraging tactics and the extent to which they give rise to restricted space use patterns are still relatively unknown. Using a combination of stochastic agent-based simulations and deterministic integro-difference equations, we developed an adaptive link (based on energy gains as a foraging currency) between memory-based patch selection and its resulting spatial distribution. We used a memory-based foraging model developed and parameterized with patch selection data of free-ranging bison Bison bison in Prince Albert National Park, Canada. Relative to random use of food patches, simulated foragers using both spatial and attribute memory are more efficient, particularly in landscapes with clumped resources. However, a certain amount of random patch use is necessary to avoid frequent returns to relatively poor-quality patches, or avoid being caught in a relatively poor quality area of the landscape. Notably, in landscapes with clumped resources, simulated foragers that kept a reference point of the quality of recently visited patches, and returned to previously visited patches when local patch quality was poorer than the reference point, experienced higher energy gains compared to random patch use. Furthermore, the model of memory-based foraging resulted in restricted space use in simulated landscapes and replicated the restricted space use observed in free-ranging bison reasonably well. Our work demonstrates the adaptive value of spatial and attribute memory in heterogeneous landscapes, and how home ranges can be a byproduct of non-omniscient foragers using past experience to minimize temporal variation in energy gains.
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>eGon</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 eGon <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> <p><strong>climate_zones_germany</strong></p> <ul> <li> <p>Climate zones in Germany</p> </li> <li> <p>source: Own representation based on DWD TRY climate zones</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>cutouts</strong></p> <ul> <li> <p>Weather data from Europe in 2011. Source: ERA5</p> </li> </ul> </li> <li> <p><strong>demand_regio_backup</strong></p> <ul> <li> <p>Electricity and heat demands</p> </li> </ul> </li> <li> <p><strong>emobility</strong></p> <ul> <li> <p>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).</p> </li> <li> <p>Reiner Lemoine Institut, June 2022</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>entsoe</strong></p> <ul> <li> <p> </p> </li> </ul> </li> <li> <p><strong>gas_data</strong></p> <ul> <li> <p>CH4 infrastructure</p> </li> <li> <p>Biogas demand</p> </li> <li> <p>CH4 demand</p> </li> <li> <p>Source: SciGRID_gas</p> </li> </ul> </li> <li> <p><strong>geothermal_potential</strong></p> <ul> <li> <p>Spatial distribution of deep geothermal potentials in Germany</p> </li> <li> <p>source: <a href="https://doi.org/10.3390/en11020332">Assessment and Public Reporting of Geothermal Resources in Germany: Review and Outlook</a></p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>household_electricity_demand_profiles</strong></p> <ul> <li> <p>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</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>household_heat_demand_profiles</strong></p> <ul> <li> <p>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</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>hydrogen_network</strong></p> <ul> <li> <p>Planned H2 infrastructure</p> </li> <li> <p>Forecast H2 demand</p> </li> <li> <p>Source: fnb-gas</p> </li> </ul> </li> <li> <p><strong>hydrogen_storage_potential_saltstructures</strong></p> <ul> <li> <p>The data are taken from figure 7.1 in Donadei, S., et al., (2020), p. 7-5..</p> </li> <li> <p>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.</p> </li> <li> <p>License: The original data are licensed under the GeoNutzV, see <a href="https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf">https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf</a></p> </li> </ul> </li> <li> <p><strong>industrial_gas_demand</strong></p> </li> <li> <p><strong>industrial_sites</strong></p> <ul> <li> <p>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.</p> </li> <li> <p>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</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>mastr_geocoding</strong></p> </li> <li> <p><strong>nep2035_version2021</strong></p> <ul> <li> <p>Data extracted from the German grid development plan - power</p> </li> <li> <p>source: Netzentwicklungsplan Strom 2035 (2021), erster Entwurf | Übertragungsnetzbetreiber (M) CC-BY-4.0</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>pipeline_classification_gas</strong></p> <ul> <li> <p>Parameters for the classification of gas pipelines</p> </li> <li> <p>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></p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>pypsa_eur</strong></p> </li> <li> <p><strong>regions_dynamic_line_rating</strong></p> <ul> <li> <p>German regions suitable to model dynamic line rating</p> </li> <li> <p>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></p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>re_potential_areas</strong></p> <ul> <li> <p>Eligible areas for wind turbines and ground-mounted PV systems.</p> </li> <li> <p>Reiner Lemoine Institut, January 2022</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>wind_offshore_status2019</strong></p> <ul> <li> <p> </p> </li> </ul> </li> <li> <p><strong>WZ_definition</strong></p> <ul> <li> <p>Definitions of industrial and commercial branches</p> </li> <li> <p>source: <a href="https://www.destatis.de/static/DE/dokumente/klassifikation-wz-2008-3100100089004.pdf">Klassifikation der Wirtschaftszweige (WZ 2008)</a></p> </li> <li> <p>Extract from Terms of Use: © Statistisches Bundesamt, Wiesbaden 2008 Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellenangabe gestattet.</p> </li> </ul> </li> <li> <p><strong>zensus_households</strong><strong> </strong></p> <ul> <li> <p>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.</p> </li> <li> <p>source: Data retrieved from <a href="https://ergebnisse2011.zensus2022.de/datenbank/online">Zensus Datenbank</a> by performing these steps:</p> <ul> <li> <p>Search for: "1000A-2029"</p> </li> <li> <p>or choose topic: "Bevölkerung kompakt"</p> </li> <li> <p>Choose table code: "1000A-2029" with title "Personen: Alter (11 Altersklassen)/Geschlecht/Größe desprivaten Haushalts - Typ des privaten Haushalts (nach Familien/Lebensform)"</p> </li> <li> <p>Change setting "GEOLK1" to "Bundesländer (16)" higher resolution "Landkreise und kreisfreie Städte (412)" only accessible after registration.</p> </li> </ul> </li> <li> <p>Extract from Terms of Use: © Statistische Ämter des Bundes und der Länder 2021, Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellennachweis gestattet.</p> </li> </ul> </li> <li> <p><strong>zensus_population</strong></p> </li> <li> <p><strong>district_heating_shares_egon.csv</strong></p> </li> </ol>
Thermal modeling of a high-energy prismatic lithium-ion battery cell and module based on a new thermal characterization methodology
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.