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374 results for “Power Data”
GODEEEP-hydro - Historical and projected power system ready hydropower data for the United States
<p>This dataset contains monthly and weekly hydropower generation and generation constraints (min, max, daily range) for over 1,400 hydropower plants in the conterminous United States. The dataset includes a historical period (1982-2019) and a future period (2020-2099) with 4 future warming scenarios.</p> <p>For more information please refer to Bracken et al. 2024, godeeep_hydro: Historical and projected power system ready hydropower data for the United States, in prep, or refer to the Github repository https://github.com/GODEEEP/tgw-hydro</p> <h3>Data description</h3> <p>The dataset contains 10 data files with the naming convention <code><scenario>_<monthly/weekly>.csv</code> where scenario can be either "historical", "rcp45cooler", "rcp45hotter", "rcp85cooler", or "rcp85hotter". "monthly" or "weekly" refers to the timestep of the data.</p> <ul> <li>datetime - The datetime stamp of the current timestep</li> <li>eia_id - An integer value with the EIA plant code that represents the facility</li> <li>plant - The name of the facility according to the EIA</li> <li>power_predicted_mwh - The total energy gnerated over the period in MWh, aka the energy target</li> <li>n_hours - The number of hours in the period, useful for converting between power and energy</li> <li>p_avg - Average power generation for the period</li> <li>p_max - Maximum allowable power generation for the period</li> <li>p_min - Minimum allowable power generation for the period</li> <li>ador - Average daily operational range for any given day in the period</li> <li>scenario - The name of the scenario, either "historical", "rcp45cooler", "rcp45hotter", "rcp85cooler", or "rcp85hotter"</li> </ul> <p>Also included is the metadata file <code>godeeep_hydro_plants.csv</code> which contains metadata for each hydropower plant that is included in the dataset. Each row in this file refers to one hydropower facility. This file has the following columns:</p> <ul> <li>eia_id - An integer value with the EIA plant code that represents the facility</li> <li>plant - The name of the facility according to the EIA</li> <li>mode - Either "Storage" or "RoR" indicating if the plant is primarily operated as a storage or ron-of-river facility</li> <li>state - Two letter U.S. state name</li> <li>lat - Latitude of the facility</li> <li>lon - Longitude of the facility</li> <li>nameplate_capacity - The total nameplate capacity of the facility according to the EIA</li> <li>nerc_region - Four letter code for the NERC region of the facility</li> <li>ba - Balacing authority of the facility</li> <li>max_param - Value of the a_{max} parameter used to derive p_max</li> <li>min_param - Value of the a_{min} parameter used to derive p_min</li> <li>ador_param - Value of the a_{ador} parameter used to derive ador</li> <li>huc2 - Two digit hydrologic unit code (HUC) which contains the facility</li> </ul> <h3>Funding</h3> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p> <p>Corresponding author:</p> <p>Cameron Bracken, cameron.bracken@pnnl.gov</p> <p>v1.1.0 - Update file naming convention</p>
Analysis data for location- and scale-invariant power transformations
<p>This repository contains various files and folders related to the machine learning experiments in a forthcoming manuscript on location- and scale-invariant power transformations.</p>
Data for Frequency to power conversion by an electron turnstile
<p>Data plotted in figures of article "Frequency to power conversion by an electron turnstile". Additional raw data and analyzing algorithms for producing them.</p>
Data set of "Effect of Proximity, Burden, and Position on the Power Quality Accuracy Performance of Rogowski Coils"
<p>The uploaded data set contains all the measurements collected during the research that led to the publication of " "Effect of Proximity, Burden, and Position on the Power Quality Accuracy Performance of Rogowski Coils"</p>
Data set from 'Sequential Feature Selection for Power System Event Classification Utilizing Wide-Area PMU Data'
<p>The increasing penetration of intermittent, nonsynchronous<br> generation has led to a reduction in total power<br> system inertia. Low inertia systems are more sensitive to sudden<br> changes, and more susceptible to secondary issues that can result<br> in large scale events. Due to the short time frames involved,<br> automatic methods for power system event detection and diagnosis<br> are required. Wide-area monitoring systems can provide<br> the data required to detect and diagnose events; however due to<br> the increasing quantity of data it is next to impossible for power<br> system operators to manually process raw data. The important<br> information is required to be extracted and presented to system<br> operators for real/near-time decision making and control. This<br> paper demonstrates an approach for the wide-area classification<br> of a number of power system events. A mixture of sequential<br> feature selection and linear discriminant analysis is adopted<br> to reduce the dimensionality of PMU data. Successful event<br> classification is obtained by employing quadratic discriminant<br> analysis on wide-area synchronized frequency, phase angle and<br> voltage measurements. The reliability of the proposed method is<br> evaluated using simulated case studies and benchmarked against<br> other classification methods.</p>
Leveraging omics data to boost the power of genome-wide association studies
<p>Summary-level GWAS data for 8 traits generated by models M0, M1 and M2 as presented in:</p> <p>Lin, Z., Knutson, K. A., & Pan, W. (2022). Leveraging omic data to boost the power of genome-wide association studies. <em>Human Genetics and Genomics Advances</em>, 100144.</p>
Replication data for: "Effectiveness of iso-inertial resistance training on eccentric and concentric power, physical performance, and risk of falls in physically active middle-older adults: a randomised controlled trial"
<p>Replication data for: "Effectiveness of iso-inertial resistance training on eccentric and concentric power, physical performance, and risk of falls in physically active middle-older adults: a randomised controlled trial"</p> <p>This folder contains 4 files:</p> <p>1) Database that contains the values for concentric and eccentric power measured with both iso-inertial and gravitational systems (Dataset_power.xlsx)</p> <p>2) Database that contains the values for the Short Physical Performance Battery (SPPB) and Get Up and Go (GUG) test (Dataset_SPPB_GUG.xlsx)</p> <p>3) R Software script used to analyse file 1 (Iso-inertial analysis_power.R)<br> <br>4) R Software script used to analyse file 2 (Iso-inertial analysis_SPPB_GUG.R)</p>
Data for coherence measurements of polaritons in thermal equilibrium reveal a power law for two-dimensional condensates
<p>All the raw data sets collected for this project are included in this submission. The code for the numerics is also included. 'Readme.text' files are included with the data sets explaining what the data sets are and how to read them. </p>
High-power in-phase and anti-phase mode emission from linear arrays of resonant-tunneling-diode oscillators in the 0.4-to-0.8-THz frequency range - data
<p>Experimental and simulation data from the paper "High-power in-phase and anti-phase mode emission from linear arrays of resonant-tunneling-diode oscillators in the 0.4-to-0.8-THz frequency range".</p>
"Power system investment optimization to identify carbon neutrality scenarios for Italy", scripts and data
<p>Script and data to reproduce the main results of "Power system investment optimization to identify carbon neutrality scenarios for Italy"</p>
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>
Codes and data for the article: Biodiversity on the Line: Life Cycle Impact Assessment of Power Lines on Birds and Mammals in Norway
<p>This repository contains all input data required to run the habitat conversion, collision, and electrocution LCIA models and reproduce the results, as well as all output data generated in various formats. The models are described in the paper "Biodiversity on the Line: Life Cycle Impact Assessment of Power Lines on Birds and Mammals in Norway" (https://doi.org/10.1088/2634-4505/ad5bfd).</p> <p>"The files "01_Get_GBIF_points.R, "02_SDMs_maxent.R" describe how to create the species distribution maps.</p> <p>"03_Data_preparation.R", "04_Pylon_cleaning.py" are to prepare and modify the raw data for the analysis. The raw data are not provided, yet links to the sources are provided either in the R codes or the paper.</p> <p>To run the models, run the "05_SHR_modelling.R" and "06_Collision_electrocution_models.R" files.</p> <p>To calculate characterization factors, run the "07_Characterisation_factors.R" file.</p> <p>To export the tables in the Supporting Information 1, run the file "08_Supporting_Information.R".</p> <p>Finally, the file "09_Sensitivity_analysis.R" performs the sensitivity analyses.</p>
A Power-Aware, Self-Adaptive Macro Data Flow Framework
<p><em><strong>Abstract: </strong>The dataflow programming model has been extensively used as an effective solution to implement efficient parallel programming frameworks. However, the amount of resources allocated to the runtime support is usually fixed once by the programmer or the runtime, and kept static during the entire execution. While there are cases where such a static choice may be appropriate, other scenarios may require to dynamically change the parallelism degree during the application execution. In this paper we propose an algorithm for multicore shared memory platforms, that dynamically selects the optimal number of cores to be used as well as their clock frequency according to either the workload pressure or to explicit user requirements. We implement the algorithm for both structured and unstructured parallel applications and we validate our proposal over three real applications, showing that it is able to save a significant amount of power, while not impairing the performance and not requiring additional effort from the application programmer.</em></p> <p>This dataset contains the raw data of the experiments and the scripts used to plot them.</p> <p> </p>
Supplementary data for "An initial assessment of the value of Allam Cycle power plants with liquid oxygen storage in future GB electricity system"
<p>The code for the Unit Commitment & Economic Dispatch model that was used in this work is available at: https://gist.github.com/vitali87/20688c161d7b5ad598b5d52b524f4585</p> <p>Sample output data can be found in the "Example Outputs.zip" file. This corresponds to the case outlined in the article that simulates a system with 5 Allam Cycle plants without Liquid Oxygen Storage, for the winter test week.</p> <p>To run the UCED model:</p> <ul> <li>Download "UC AIMMS Allam Cycle Model" code from the github and save as an AIMMS project file.</li> <li>Save the file in a folder that contains all the necessary input datasets, found in the "Universal Inputs for UCED Model.zip" file, and the example outputs, found in the "Example Outputs.zip" file, which are to be overwritten. Do not change the name of the input or output files.</li> <li>Open the project and execute the following procedures: <ul> <li>"Main Initialisation" - to initialise the problem</li> <li>"Read from Excell" - to read data from the input files</li> <li>"Main Execution" - to begin running the problem</li> </ul> </li> <li>Once the run is complete, execute "Run External Procedure" to overwrite the output files with the new data.</li> </ul> <p>To change the test week:</p> <ul> <li>Open "Demand Profiles" in 'sets' and change the set definition. Enter "C1" for the winter week and "C21" for the summer week. Another week can alternatively be selected. For example, entering "C45" would allow the model to run with the weather and demand data from the 45th week in the year 2010. </li> <li>Save and close the set.</li> </ul> <p>To change the number of plants in the system:</p> <ul> <li>Open "PCCSGenerators" in 'sets' and change the set definition. To run with 5 Post Combustion Capture plants, end the list of generators after plant number 5 by commenting the remaining plants. This is done by using "!" after the 5th plant name in the string. Then save and close the set.</li> <li>Repeat the above step for the "ACGenerators" and "AirSeparationUnits" sets, to change the number of Allam Cycle plants in the system.</li> </ul> <p>To add or remove oxygen storage capability from the Allam Cycle plants:</p> <ul> <li>Open the "Main Initialisation" procedure.</li> <li>To run the model without oxygen storage: <ul> <li>make sure the following command is stated: "AC_ASU_coupled := 0;"</li> <li>save and close the procedure</li> </ul> </li> <li>To run the model with oxygen storage: <ul> <li>make sure the following is command is stated: "AC_ASU_coupled := 1;"</li> <li>make sure that the number, 'X', of "map_AC_to_ASU('Gas_CCS_AC_X') := 'ASU_X';" commands that are active matches the number of active Allam Cycle plants in the model</li> <li>save and close the procedure.</li> </ul> </li> </ul>
6T and 8T FinFET SRAM Power Gating Data
<p>Graphs and underlying data for 6T and 8T FinFET SRAM schemes using power gating. These data and graphs accompany "Effective Low Leakage 6T and 8T FinFET SRAMs: Using Cells with Reverse-Biased FinFETs, Near-Threshold Operation, and Power Gating" submitted to <em>IEEE Transactions on Circuits and Systems II: Express Briefs</em> on October 1, 2018.</p>
Viet Nam Technology Catalogue - Technology data input for power system modelling in Viet Nam
<p>Today, innovations and technology improvements within renewable energy are taking place at a very rapid pace. Long-term energy planning is very dependent on cost and performance of future energy producing technologies.<br> This technology catalogue provides estimates of costs and performance for a wide range of power producing technologies, thereby building one of the key inputs to good energy planning in Vietnam.<br> Due to the multi-stakeholder involvement in the data collection process, the technology catalogue contains data that have been scrutinised and discussed by a broad range of relevant stakeholders including the Ministry of Industry and Trade – MOIT, Vietnam Electricity – EVN, independent power producers, local and international consultants, organizations, associations and universities. This is essential because a main objective is to produce a technology catalogue which is well anchored amongst all stakeholders.<br> The technology catalogue will assist the long-term energy modelling in Vietnam and support government institutions, private energy companies, think tanks and others with a common and broadly recognized set of data for electricity producing technologies in Vietnam in the future.</p>
Power engineering cost data
<div> <div>This data set contains information about various costs, budgets, materials, processes, equipment, labor and other aspects related to power engineering projects. Specifically, it is mainly used for the analysis, evaluation and prediction of project cost, so as to help project managers, engineers, cost engineers and other personnel engaged in power engineering to make reasonable decisions and plans.</div> </div>
Data from: Whole-genome phylogenetic reconstruction as a powerful tool to reveal homoplasy and ancient rapid radiation in waterflea evolution
<p>The Supplementary Material (and text) to Van Damme et al., contains 10 Supplementary Figures (Figs S1-S10), 5 Supplementary Tables (Tables S1-S5), Supplementary Materials and Methods (ST1), Supplementary Discussion (ST2) and a complete reference list to the manuscript and supplement (Supplementary References SR1). All Supplementary material and text have been peer-reviewed as part of the manuscript. The supplementary discussion provides an additional framework including the importance of the findings of the phylogenomic study for the interpretation of evolution in the Cladocera.</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>
AALTO - Power Angular Measurements and Ray Tracing Simulations at Sub-THz Frequencies in Corridor - DATA
<p>The data set includes simulation results from radio propagation modelling of TERAWAY links (at 90, 95 and 100 GHz) in realistic university corridor environment. The modelling is performed using a Ray Tracing Tool developed in MATLAB environment at Aalto University. Ray tracing technique used in this tool is based on Image Theory (IT) algorithm. Unlike a quasi three-dimensional environment, it supports ray tracing in full three dimension.</p> <p>This data set contains propagation modelling results of the TERAWAY link. Output data includes (but is not limited to): Multipath component IDs, Path Distance (meter), Angle of Arrival AoA (degree), Angle of Departure AoD (degree), Direction of Arrival DoA (degree), Direction of Departure DoD (degree), E-Field (Volt/meter), H-Field (Ampere/meter), Phase (Radians), Power (Watts), Number of reflections a path experienced, Number of diffractions a path experienced, information that is it ground reflected path or not, Receiver location (x and y coordinates), information that is it rooftop path or not.</p>
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Annotated Behaviour and Observability Dataset (ABODe)
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International Brain Laboratory public data
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OpenNeuro
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