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Dataset results
210 results for “Energy modeling”
eELib: Open-Source Model Library for Prosumer Power Systems and Energy Management Strategies (data)
<p>Dataset and results used for the simulations in following publication:</p> <p>Carsten Wegkamp, Henrik Wagner, Eike Niehs, Julien Essers, Marcel Lüdecke, Mattias Hadlak, Bernd Engel:<br>"<strong>eELib: Open-Source Model Library for Prosumer Power Systems and Energy Management Strategies</strong>",<br>Open Source Modelling and Simulation of Energy Systems (OSMSES) 2024, Vienna, Austria, 2024</p> <p> </p> <p>This contains the input (scenario) files for the building & grid scenario and the results of the two simulations.<br>It uses the elenia Energy Library (eELib) with release version 1.0.0: https://gitlab.com/elenia1/elenia-energy-library</p>
Modeling and simulation of a new Urban Lightweight Electric Vehicle concept based on the optimized use of renewable energies and the reduction of CO2 emissions
<p>This work has produced a series of scientifc contributions. This library develops different mathematical expressions and assumptions for the dynamic modelling of an smart-grid located within a solar-powered ULEV are derived. The code was developed using Dymola</p>
DirtyGrid: 3D dust radiative transfer modeling of spectral energy distributions of dusty stellar populations
<p>Output global SEDs of a large grid of 3D stellar+dust radiative transfer models spanning the range of star formation and dust contents of regions of galaxies.</p> <p>Paper describing the DirtyGrid is Law, Gordo, & Misset (2018, ApJ, submitted)</p> <p>Code to make to access this data at: https://github.com/karllark/pydirtygrid</p>
Dataset: Direct insertion of NASA Airborne Snow Observatory-derived snow depth time-series into the iSnobal energy balance snow model
<p>This dataset is a companion to the submitted WRR publication entitled ‘Direct insertion of NASA Airborne Snow Observatory-derived snow depth time-series into the iSnobal energy balance snow model’. The file structure is organized as follows:</p> <ul> <li><strong>ASO_50m_depth_surfaces</strong> - This folder contains the Airborne Snow Observatory lidar-derived snow depth products aggregated to 50m gridded spatial resolution. Each file is titled with a date such as ‘TB<em>YYYYMMDD</em>_SUPERsnow_depth.asc’. The coordinates are in UTM zone 11N and use the WGS84 coordinate system.</li> <li><strong>static_grids</strong> <ul> <li>Static grids are used in each of the subsequent folders and are not changed between years.</li> <li>init0000.ipw <ul> <li>Initialization file to begin the model run. Contains the digital elevation model in band 1, surface roughness raster in band 2, and zeroed images of snow properties in bands 3-7.</li> </ul> </li> <li>maxus.nc <ul> <li>netCDF file of 72 separate images of maximum upwind slope for all upwind directions from 0 (north) to 355 degrees in 5-degree increments. Derived using Adam Winstral’s Sx algorithm.</li> </ul> </li> <li>tuolx_dem_50m.ipw <ul> <li>Digital elevation model from ASO snow-free acquisition aggregated to 50m gridded spatial resolution. Same information as band 1 in the init0000.ipw file.</li> </ul> </li> <li>tuolx_hetchy_mask_50m.ipw <ul> <li>Basin mask of the Tuolumne River Basin above Hetch Hetchy Reservoir. Out-of-basin cells denoted as 0, and in-basin cells denoted as 1.</li> </ul> </li> <li>tuolx_vegheight_50m.ipw <ul> <li>Vegetation height raster in meters. Derived from NLCD dataset of vegetation type..</li> </ul> </li> <li>tuolx_vegk_50m.ipw <ul> <li>Emissivity of the vegetation canopy. Derived from NLCD dataset of vegetation type.</li> </ul> </li> <li>tuolx_vegnlcd_50m.ipw <ul> <li>Vegetation type from the National Land Cover Database.</li> </ul> </li> <li>tuolx_vegtau_50m.ipw <ul> <li>Fractional transmissivity of the vegetation canopy. Derived from NLCD dataset of vegetation type.</li> </ul> </li> </ul> </li> <li><strong>level1_raw_data</strong> <ul> <li>{Hourly data interpolated to nearest hour from downloaded raw data (CDEC/MesoWest)}</li> <li>air_temp_level1.csv</li> <li>precip_accum_level1.csv</li> <li>relative_humidity_level1.csv</li> <li>solar_radiation_level1.csv</li> <li>wind_direction_level1.csv</li> <li>wind_speed_level1.csv</li> </ul> </li> </ul> <p>The directories for each water year contain the configuration file for that year along with the vector meteorological data from measurement sites and site metadata in .csv format.</p> <ul> <li><strong>wy2013</strong></li> <li><strong>wy2014</strong></li> <li><strong>wy2015</strong></li> <li><strong>wy2016</strong> <ul> <li> <ul> <li>backup_config.ini {Initialization file used to distribute station data over a regular grid for each water year.}</li> <li>air_temp.csv</li> <li>cloud_factor.csv</li> <li>metadata.csv</li> <li>precip.csv</li> <li>vapor_pressure.csv</li> <li>wind_direction.csv</li> <li>wind_speed.csv</li> <li><strong>data/</strong> <ul> <li>[subdirectory containing all future created forcing grid files]</li> </ul> </li> <li><strong>runs/</strong> <ul> <li>[subdirectory containing all <em>iSnobal</em> output files in addition to reinitialization scripts for ASO snow depth updates]</li> </ul> </li> </ul> </li> </ul> </li> </ul>
SET-NAV: WP5: Invert modelling output for the building sector final energy demand and cost data
<p>This data set contains the Invert modelling results for final energy demand for space heating, cooling and hot water in buildings; hourly data for district heating and electricity (for different technologies) for 3-4 building types; annual data for the other energy carriers.</p> <p>It also contains all annual cost (Annuity of investments, O&M, fuel cost ) of electricity generation and / or heat generation and considered efficiency measures.</p> <p>This data is also available and visualised in our dedicated SET-NAV open data platform: The SET-NAV Scenario Explorer: https://data.ene.iiasa.ac.at/set-nav/#/workspaces</p>
Data bundle for powerd-data: A transparent and reproducible data processing pipeline for energy system modeling based on egon-data
<div> <p><strong>powerd-data</strong> provides a transparent and reproducible open data based data processing pipeline for generating data models suitable for energy system modeling. Is is a fork from the open-source tool <strong>egon-data</strong>. </p> <p>powerd-data and egon-data retrieve and process 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>district_heating_shares: <ul> <li>Assumed district heating share for all European countries in 2050</li> <li>Source: Own representation</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>egon_demandregio_cts_ind:<br> <ul> <li>Industrial and CTS demands per branch and NUTS3 region in Germany for the year 2050</li> <li>Source: egon-data, based on data from DemandRegio disaggregator tool</li> <li>License: Data license Germany – © FfE 2019, © Statistisches Bundesamt (Destatis), 2008-2017 – version 2.0</li> </ul> </li> <li>industrial_gas_demand: <ul> <li>This folder contains 5 files. The files CH4_for_industry_eGon100RE.json, CH4_for_industry_eGon2035.json, H2_for_industry_eGon100RE.json and H2_for_industry_eGon2035.json contain the industrial hourly demands for hydrogen and methane in NUTS3 resolution for the scenarios eGon100RE and eGon2035. The file region_corr.json provides information that make it possible to correlate each load to a geographical position.</li> <li>License: Attribution 4.0 International (CC BY 4.0) © FfE, eXtremOS Project</li> </ul> </li> </ol> <p> </p> </div>
The effect of uncertainty in humidity and model parameters on the prediction of contrail energy forcing
<p>Previous work has shown that while the net effect of aircraft condensation trails (contrails) on the<br>climate is warming, the exact magnitude of the energy forcing per meter of contrail remains uncertain.<br>In this paper, we explore the skill of a Lagrangian contrail model (CoCiP) in identifying flight<br>segments with high contrail energy forcing. We find that skill is greater than climatological<br>predictions alone, even accounting for uncertainty in weather fields and model parameters.</p> <p>We estimate the uncertainty in weather by using the ensemble ERA5 weather reanalysis from the European<br>Centre for Medium-Range Weather Forecasts (ECMWF) as Monte Carlo inputs to CoCiP. We unbias and correct<br>under-dispersion on the ERA5 humidity data by forcing a match to the distribution of in situ humidity<br>measurements taken at cruising altitude. We set aside CoCiP energy forcing estimates calculated using<br>one of the ensemble members as a proxy for ground truth, and report the skill of CoCiP in identifying<br>segments with large positive proxy energy forcing. We further estimate the uncertainty in the model<br>parameters in CoCiP by performing Monte Carlo simulations with CoCiP model parameters drawn from<br>uncertainty distributions consistent with the literature.</p> <p>When CoCiP outputs are averaged over seasons to form climatological predictions, the skill in<br>predicting the proxy is 44%, while the skill of per-flight CoCiP outputs is 84%. If these results carry<br>over to the true (unknown) contrail EF, they indicate that per-flight energy forcing predictions can<br>reduce the number of potential contrail avoidance route adjustments by 2x, hence reducing both the cost<br>and fuel impact of contrail avoidance.</p>
Dataset related to "Material recycling in energy system modeling: a review and showcase"
<p>This dataset has been generated and used for the publication:</p> <blockquote> <p>Zwickl-Bernhard, S., 2024. Material recycling in energy system modeling: a review and showcase. ... DOI: ...</p> </blockquote> <p>The corresponding code is published on *Github. #add link after publication</p> <p>The dataset includes:</p> <ol> <li>Compiled data for manufacturing costs of Solar Modules and Wind Turbines in the EU (manufacturing costs in the EU.xlsx)</li> <li>Compiled data for modified prices (modified prices.xlsx)</li> <li>Additional Data (scalars.xlsx)</li> <li>Sources (sources.txt)</li> <li>Compiled data for yearly capacity of Solar Modules and Wind Turbines (vectors_capacity.xlsx)</li> <li>Compiled data for yearly costs of Solar Modules and Wind Turbines (vectors_costs.xlsx)</li> <li>Compiled data for yearly manufacturing costs of Solar Modules and Wind Turbines (vectors_manufacturing.xlsx)</li> </ol>
Advancing Large Language Models through Story Energy, Universal Harmony Energy, and SA-UUH-UPP
<p><span>In this groundbreaking exploration of AI, we unveil how Story Energy, Universal Harmony Energy, and the SA-UUH-UPP framework could revolutionize large language models (LLMs). Discover how these advanced concepts push AI beyond current boundaries, enabling deeper contextual understanding, energy-efficient models, and steps toward self-awareness. Whether you’re an AI researcher, developer, or enthusiast, this video provides insights that could redefine the future of AI. Watch now to dive into the next frontier of artificial intelligence!</span></p>
Dataset and R code support the manuscript titled "Streamlining Linear Free Energy Relationships of Proteins through Dimensionality Analysis and Linear Modeling"
<p>This dataset and R code support the manuscript titled "Streamlining Linear Free Energy Relationships of Proteins through Dimensionality Analysis and Linear Modeling" submitted to the Journal of Chemical Information and Modeling.</p> <p>Table S 1: Chemicals with their experimental values of logKch , and values of logKow and<span> </span>logKaw used to calibrate chicken muscle protein-water 2p-LFER model.</p> <p>Table S 2: Chemicals with their experimental values of logKfish and values of logKow and<span> </span>logKaw used to calibrate fish muscle protein-water 2p-LFER model.</p> <p>Table S 3: Chemicals with their experimental values of logKBSA and values of logKow and<span> </span>logKaw used to calibrate bovine serum albumin-water 2p-LFER model.</p> <p>Table S 4: Chemicals with their experimental values of logKpw and values of logKow and<span> </span>logKaw used to calibrate combined chicken and fish muscle protein-water 2p-LFER model.</p> <p>Table S 5: Diversity of data for logKpw.</p> <p>Table S 6: Diversity of data for logKBSA.</p> <p>Table S 7: Comparison of Experimental and 2p-LFER Predicted Partition Coefficients for ionizable PFAS Compounds.</p> <p>Table S 8: List of neutral fluorotelomer PFAS Compounds.</p> <p>Table S 9: List of experimental in vivo and in vitro partitioning data for different tissues and species.</p> <p>Table S 10: List of experimental Milk-water partition coefficient and predicted values of Milk-water partitioning.</p> <p>Table S 11: Training set for logKpw</p> <p>Table S 12: Validation set for log Kpw</p> <p>Table S 13: Training set for log KBSA</p> <p>Table S 14: Validation set for log KBSA</p>
Code and data for publication "pyGRETA, pyCLARA, pyPRIMA: A pre-processing suite to generate flexible model regions for energy system models"
<p>This dataset contains the code of the three pre-processing tools <a href="https://github.com/tum-ens/pyGRETA">pyGRETA</a>, <a href="https://github.com/tum-ens/pyPRIMA">pyPRIMA</a> and <a href="https://github.com/tum-ens/pyCLARA">pyCLARA</a> and an examplary database for the scope of Austria.</p> <p>To run the code with full functionality additional data is needed. Check the documentation of the tools for further information.</p> <p> </p> <p>Sources for data can be found here: </p> <p>pyGRETA: https://pygreta.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p> <p>pyPRIMA: https://pyprima.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p> <p>pyCLARA: https://pyclara.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p>
Dataset for "Modeling Neighborhood-Scale Shallow Geothermal Energy Utilization - A Case Study in Berlin"
<p>Dataset with prepared ogs simulations used in the paper "Modeling Neighborhood-Scale Shallow Geothermal Energy Utilization - A Case Study in Berlin".</p> <p>Plese read the README file in the folder first.</p>
An Assessment of Nonhydrostatic and Hydrostatic Dynamical Cores at Seasonal Time Scales in the Energy Exascale Earth System Model (E3SMv1)
<p>This is the companion data for the manuscript of the same title, submitted to Journal of Advances in Modeling Earth Systems on 09/03/2021. </p> <p><strong>summer: </strong>this folder contains part of the model outputs I ran on NERSC Cori in 2020-2021 corresponding to the summer simulations in the manuscript.</p> <p><strong>winter: </strong>this folder contains part of the outputs I ran on NERSC Cori in 2020-2021 corresponding to the winter simulations in the manuscript.</p> <p><strong>ne256: </strong>this folder contains part of the outputs I ran on NERSC Cori in 2021 corresponding to the ne256 simulations in the manuscript.</p> <p><strong>bubble</strong>: this folder includes the namelists used in the rising bubble experiments.</p> <p><strong>script: </strong>this folder includes an example of a script to generate the realistic SCREAM simulation </p> <p> </p> <p>Unfortunately, model outputs are too large (~ 30 TB). Therefore, I only provide mean data, used directly to generate figures, in this repository. All model output are archived on tape at NERSC. </p> <p>For more details, refer to the manuscript, or contact me (wrliu@ucdavis.edu). </p>
Dataset for "Convex modeling of pumps in order to optimize their energy use" article
<p>This is the data set about pump optimization used in the submitted article "Convex modeling of pumps in order to optimize their energy use" . The proposed optimization method is shown, and also the two methods to which it is compared to.</p> <p> </p>
Data for the Eastern African power pool's energy systems model, developed in OSeMOSYS
<p>This repository consists of the following datasets</p> <p>1. EAPP_reference scenario_datafile.DD- This dataset is a model file that needs to be used with the code available in this <a href="https://github.com/KTH-dESA/OSeMOSYS/blob/master/OSeMOSYS_GNU_MathProg/osemosys_short.txt">GitHub</a> link. This data file (in concurrence with the OSeMOSYS code) can be used to create a linear programming file (LP file) to be solved using any mathematical optimisation solver like GLPSOL/C-PLEX/GUROBI/CBC.</p> <p>2. Main article_EAPP_data for figures.xlsx- This excel file contains the base data used to illustrate the figures in the main article.</p> <p>3. Supplementary article_EAPP_data for figures.xlsx- This excel file contains the base data used to illustrate the figures in the supplementary article.</p>
Grey model analysis of vehicle population, road transport energy consumption, and vehicular emissions
<p>The files provide additional information to the paper “Grey model analysis of vehicle population, road transport energy consumption, and vehicular emissions". The supporting data file contains excel sheets of data used in the analysis, and the supporting information file provides some assumptions, background information and other results not included in the paper</p>
Supporting Data and Guidance: Modeling policy pathways to maximize renewable energy growth and investment in Democratic Republic of the Congo using OSeMOSYS
<p>This repository contains data files and guidance documents that are supplementary materials to accompany the policy paper "Modeling policy pathways to maximize renewable energy growth and investment in Democratic Republic of the Congo using OSeMOSYS" available on Research Square here: <a href="https://www.researchsquare.com/article/rs-2702275/v1">https://www.researchsquare.com/article/rs-2702275/v1</a></p>
Data Collection and Manipulation Template for MAED (Model for Analysis of Energy Demand)
<p>A Data Collection and Manipulation Template for MAED (Model for Analysis of Energy Demand). </p>
CCG: Beyond the Dams: Combatting Hydropower Over-reliance & Securing Pathways for a Low-carbon Future for Laos' Electricity Sector using OSeMOSYS (Open-Source Energy Modelling System)
<p>Seven clicSAND scenario files for <strong>Beyond the Dams: Combatting Hydropower Over-reliance & Securing Pathways for a Low-carbon Future for Laos' Electricity Sector using OSeMOSYS (Open-Source Energy Modelling System).</strong> </p> <p><strong>How to Visualise Results Online and Offline</strong> outline the steps required to re-run the scenarios on OSeMOSYS Cloud</p> <p><strong>Scenario Short Note</strong> outlines the steps to replicate the analysis and rebuild the scenarios</p> <p><strong>Annex - Input Data and Assumptions</strong> listing the data sources and assumptions in the scenarios</p>
Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution (data)
<p>Data for "Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution"</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.