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86 results for “Power Modeling”
Dataset for "Estimating the offshore wind power potential of Portugal by utilizing gray-zone atmospheric modeling" article
<p>This dataset is used for analysis and visualization, that supports the article titled "Estimating the offshore wind power potential of Portugal by utilizing gray-zone atmospheric modeling", which has been accepted for publication in the Journal of Renewable Sustainable Energy.</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>
Modeling Decarbonization Pathways in the Power Sector in Developing Countries: The case of Colombia (EMP-LAC 2023) - Dataset
<p>Dataset of the project Modeling Decarbonization Pathways in the Power Sector in Developing Countries: The case of Colombia (EMP-LAC 2023) - Dataset</p>
Brazil - Power Model 2050
<p><strong>Installation and running the model</strong></p><p>It is necessary to install Calliope to run the model. Instructions for installation and running the model are available at:<a href="https://calliope.readthedocs.io/">https://calliope.readthedocs.io/</a>.</p><p><strong>Temporal resolution</strong></p><p>The temporal resolution of the model is 8 hours by default. You can set the model with another resolution in the "overrides" file: </p><p>time_resampling:</p><p> model.time: {function: resample, function_options: {'resolution': '8H'}}</p><p> </p><p>Please be aware that running the model might be computationally expensive. The model contains data from one year. If you wish to test the model, you can indicate a shorter time range in the "overrides" file > weather years. For example, you can select a subset of ten days of data:</p><p> year_2010:<br> model.subset_time: ['2010-01-01', '2010-01-10']</p><p> </p><p><i>Weather year</i></p><p>Weather years include data from 2000 to 2019.</p><p><strong>Scenarios</strong></p><p>The scenario names are structured as follows: route + policy + year:</p><p>Routes: </p><p>1) Baseline</p><p>2) Limited electrification (elec. stage 1)</p><p>3) Intensive electrification (elec. stage 2)</p><p>4) Net zero</p><p>Policy:</p><p>1) Default (status quo)</p><p>2) Land constraints (exclusion of relevant ecological lands) (LC)</p><p>3) 100% renewable (RE) - phase-out fossil fuels</p><p>4) Land constraints (LC) + 100% RE</p><p>Example:</p><p>scenario_netzero_banned_2019 (route=netzero, policy= phase-out fossil fuels)</p><p> </p>
Monitoring of postpartum body condition at the cow and herd levels: assessing explanatory and predictive power of disease risk models
<p>Objectives</p> <p>1- To define the herd threshold for cows with poor body condition based on its predictive capacity for disease risk at the herd level, and</p> <p>2- to estimate the impact measures on disease rates due to body condition indicators in transition period.</p> <p>Two commercial grazing dairy herds (Herd A=5.034 and herd B=7.965 lactations) from Argentinean Pampa region were used to perform a longitudinal retrospective study during a 4-year period (2014 –2017).Health, reproductive and body condition score (BCS) records were gathered. The BCS (5-point scale) was performed at calving and at the time of reproductive release. The difference between both measures of BCS was used to assess the body condition loss (∆BCS). All the cows not bred by 70 DIM were checked for anestrus.Calving cohorts of 21-day were defined at each herd and parity group through the entire study period. The frequency of cows with BCS<3 or ∆BC>-0.5 at each cohort were calculated and used to define quartiles through whole study period. Quartiles were used, one at a time, as threshold to dichotomize the cohorts to predict the risk that a cohort has a frequency of anestrus over the median.The higher AUC was used as selection criterium to determine the herd level threshold at each HERD and PARITY level. The population attributable fraction (AFP) of anestrus rate to body condition indicators at each cohort was calculated, for every HERD and PARITY level. </p>
Dynamic stability of synthetic power grid models
<p>This repository contains three datasets of synthetic power grids and their dynamic stability.</p> <p>1. 10,000 grids of size 20 (ds20) stored in dataset020.zip</p> <p>2. 10,000 grids of size 100 (ds100) stored in dataset100.zip</p> <p>3. 1 Texan power grid model with 1,910 nodes (texas) stored intexas.zip</p> <p> </p> <p>There are three tasks SNBS (regression) and the identification of troublemakers using regression and thresholding based on the maximum frequency deviation or classification based on binary targets.</p>
Hybrid Machine Learning Model for Ultra-Short-Term Wind Power Forecasting with Multi-Model Training Approach
<p>This is the core data code of the "<strong>Hybrid Machine Learning Model for Ultra-Short-Term Wind Power Forecasting with Multi-Model Training Approach".</strong></p>
MIMOSA: A resource consisting of improved methylome imputation models increases power to identify CpG site-phenotype associations
<p>MIMOSA DNA methylation prediction models, set up for MWAS. To run MWAS with this resource, see the tutorial here: <a href="https://github.com/ChongWuLab/MIMOSA">https://github.com/ChongWuLab/MIMOSA</a></p>
Data from: Powerful yet challenging: Mechanistic Niche Models for predicting invasive species potential distribution under climate change
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Dynamic inferential NOx emission prediction model with delay estimation for SCR de-NOx process in coal-fired power plants
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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 from the water-power nexus modelling of the Southern African Power Pool (SAPP)
<p>Input datasets and simulation results of the Southern African Power Pool (SAPP) simulations conducted with <a href="http://www.dispaset.eu/en/latest/#">Dispa-SET</a>, The underlying assumptions and the model are described in this technical report.</p> <p>Full citation of the technical report:</p> <p>Busch, S., De Felice, M. and Hidalgo Gonzalez, I., Analysis of the water-power nexus in the Southern African Power Pool, EUR 30322 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-21015-3 (online), doi:10.2760/920794 (online), JRC121329.</p>
Data from: Successful by chance? the power of mixed models and neutral simulations for the detection of individual fixed heterogeneity in fitness components
Heterogeneity in fitness components consists of fixed heterogeneity due to latent differences fixed throughout life (e.g. genetic variation), and dynamic heterogeneity generated by stochastic variation. Their relative magnitude is crucial for evolutionary processes, as only the former may allow for adaptation. However, the importance of fixed heterogeneity in small populations has recently been questioned. Using neutral simulations (NS), several studies failed to detect fixed heterogeneity, thus challenging previous results from mixed models (MM). To understand the causes of this discrepancy, we estimate the statistical power and false positive rate of both methods, and apply them to empirical data from a wild rodent population. While MM show high false positive rates if confounding factors are not accounted for, they have high statistical power to detect real fixed heterogeneity. In contrast, NS are also subject to high false positive rates, but have always low power. Indeed, MM analyses of the rodent population data show significant fixed heterogeneity in reproductive success, whereas NS analyses do not. We suggest that fixed heterogeneity may be more common than is suggested by NS, and that NS are useful only if more powerful methods are not applicable and if they are complemented by a power analysis.
Innovative Strategies for Blood-Brain Barrier (BBB) Permeability Modeling: Harnessing the Power of Machine Learning-based q-RASAR Approach
<p>In the current research, we have unveiled an advanced technique termed the quantitative Read-Across Structure-Activity Relationship (q-RASAR) framework to harnesses the power of machine learning (ML) for significantly enhancing the precision of predictions related to blood-brain barrier (BBB) permeability. It is important to emphasize that the central objective of this study is not to introduce another model for predicting BBB permeability. Instead, our focus is on highlighting the improvement in predicting the BBB permeability of organic compounds by introducing the q-RASAR approach. This innovative methodology strives to enhance the precision of evaluating neuropharmacological implications and streamline the drug development process. In this investigation, we developed an ML-based q-RASAR PLS model using a dataset comprising 1012 compounds of diverse classes of heterocyclic and aromatic hydrocarbons, obtained from the freely accessible B3DB database (accessible at <a href="https://github.com/theochem/B3DB">https://github.com/theochem/B3DB</a>) to predict BBB permeability during the lead discovery phase for central nervous system (CNS) drugs. The model's predictive capability underwent validation using two external sets, encompassing a total of 1,130,315 compounds, including synthetic compounds and natural products (NPs) for data gap filling and other two external sets comprising 116 drug-like/drug compounds from FDA and ChEMBL databases to assess the model's reliability. This study aimed to bridge the data gap by employing a predictive model to estimate the impact of brain-plasma concentration ratios on BBB permeability for both synthetic compounds and natural products (NPs). To further enhance predictability, we have developed various other ML-based q-RASAR models. The insights from the developed model highlight the pivotal roles played by hydrophobicity, electronic effects, degree of ionization and steric factors as essential features facilitating the traversal of the blood-brain barrier. This research not only advances our understanding of the molecular determinants influencing the permeability of central nervous system drugs but also establishes a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in the realms of drug development and design.</p>
Development of equivalent circuit model for state of power estimation of NMC-based Li-ion cell
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Model Development for State-of-Power Estimation of Large-Capacity Nickel-Manganese-Cobalt Oxide-Based Lithium-Ion Cell Validated Using a Real-Life Profile
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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>
Datasets for "Detecting rhythmic spiking through the power spectra of point process model residuals"
<p>This repository contains synthetic and empirical datasets that were analyzed in the preprint "Detecting rhythmic spiking through the power spectra of point process model residuals."</p> <p>Relative to the previous version (1.0.0), this new version (1.0.1) only entails changes in the naming of some folders, and the text of an explanatory text file. The changes reflect the reordering of the figures in the newest version of the manuscript.</p>
OpenFoam model output for "A Fuel Cell Power Supply System Equipped with Artificial Gill Membranes for Underwater Applications"
<p>Dataset of numerical experiments carried out with OpenFOAM v 10 as used in the manuscript "A Fuel Cell Power Supply System Equipped with Artificial Gill Membranes for Underwater Applications" by Lucas Merckelbach and Prokopios Georgopanos.</p> <p> </p>
SimBench - Electrical Power System Benchmark Models
<p>SimBench (<a href="https://www.simbench.net">www.simbench.net</a>) is a research project to create a "simulation database for uniform comparison of innovative solutions in the field of network analysis, network planning and operation", which was conducted for three and a half years from 1.11.2015 to 30.04.2019. It was part of the German Federal Government's 6th Energy Research Program "Research for an Environmentally Friendly, Reliable and Affordable Energy Supply". The project was carried out by the University of Kassel, the Fraunhofer IEE, the RWTH Aachen University and the Technical University of Dortmund in accordance with the authors mentioned above. The project, coordinated by the University of Kassel, was supported by the professional advisory from six German distribution network operators: DREWAG NETZ GmbH, Energie Netz Mitte GmbH, ENSO NETZ GmbH, Netze BW GmbH, Syna GmbH and Westnetz GmbH.</p> <p>The objective of the research project SimBench is the development of a benchmark data set to support research in grid planning and operation. SimBench Grid differs from other benchmark grids under the following key points:</p> <ul> <li>Consideration of a wide range of use cases during the development of data sets</li> <li>Provision of grid data for low voltage (LV), medium voltage (MV), high voltage (HV), extra high voltage (EHV) as well as design of data sets for a suitable interconnection of a grid among different voltage levels for cross-level simulations</li> <li>Ensuring highreproducibility and comparability by providing clearly assigned load and generation time series</li> <li>Validation of the suitability of the data sets with simulation, deliberately determined grid states including suitable dimensioning of grid assets</li> </ul> <p>In total SimBench provides 13 unique electrical power system grids (EHV: 1, HV: 2, MV: 4, LV: 6). Since SimBench is enables multi-voltage simulations, this dataset includes not only 13 folder but many more. Each excerpt of the complete dataset, composed to a folder, is distinctively named by the SimBench code.</p> <p>For further information, please visit <a href="https://www.simbench.net">www.simbench.net</a> and the documentation published there.</p>
ScienceDex guides
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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.