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130 results for “Power Systems”
data for "On the tuning and performance of Stand-Alone Large-Power PV irrigation systems"
<p>These data were used in article "On the tuning and performance of Stand-Alone Large-Power PV irrigation systems" ( <a href="https://doi.org/10.1016/j.ecmx.2021.100175">https://doi.org/10.1016/j.ecmx.2021.100175</a>)</p>
Open-source quality control routine and multi-year power generation data of 175 PV systems
<p><strong>Description</strong></p> <p>The repository contains an extensive dataset of PV power measurements and a python package (qcpv) for quality controlling PV power measurements. The dataset features four years (2014-2017) of power measurements of 175 rooftop mounted residential PV systems located in Utrecht, the Netherlands. The power measurements have a 1-min resolution.</p> <p><strong>PV power measurements</strong></p> <p>Three different versions of the power measurements are included in three data-subsets in the repository. Unfiltered power measurements are enclosed in <em>unfiltered_pv_power_measurements.csv</em>. Filtered power measurements are included as <em>filtered_pv_power_measurements_sc.csv </em>and<em> filtered_pv_power_measurements_ac.csv</em>. The former dataset contains the quality controlled power measurements after running single system filters only, the latter dataset considers the output after running both single and across system filters. The metadata of the PV systems is added in<em> metadata.csv</em>. This file holds for each PV system a unique ID, start and end time of registered power measurements, estimated DC and AC capacity, tilt and azimuth angle, annual yield and mapped grids of the system location (north, south, west and east boundary).</p> <p><strong>Quality control routine</strong></p> <p>An open-source quality control routine that can be applied to filter erroneous PV power measurements is added to the repository in the form of the Python package qcpv (<em>qcpv.py</em>). Sample code to call and run the functions in the qcpv package is available as <em>example.py.</em></p> <p><strong>Objective</strong></p> <p>By publishing the dataset we provide access to high quality PV power measurements that can be used for research experiments on several topics related to PV power and the integration of PV in the electricity grid.</p> <p>By publishing the qcpv package we strive to set a next step into developing a standardized routine for quality control of PV power measurements. We hope to stimulate others to adopt and improve the routine of quality control and work towards a widely adopted standardized routine. </p> <p><strong>Data usage</strong></p> <p>If you use the data and/or python package in a published work please cite: <em>Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2022. Open-source quality control routine and multi-year power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy.</em></p> <p><strong>Units</strong></p> <p>Timestamps are in UTC (YYYY-MM-DD HH:MM:SS+00:00).</p> <p>Power measurements are in Watt.</p> <p>Installed capacities (DC and AC) are in Watt-peak.</p> <p><em><strong>Additional information</strong></em></p> <p>A detailed discussion of the data and qcpv package is presented in: <em>Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2022. Open-source quality control routine and multi-year power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy. Corrections are discussed in: Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2024. </em><em>Erratum: Open-source quality control routine and multiyear power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy.</em></p> <p><strong>Acknowledgements </strong></p> <p>This work is part of the Energy Intranets (NEAT: ESI-BiDa 647.003.002) project, which is funded by the Dutch Research Council NWO in the framework of the Energy Systems Integration & Big Data programme. The authors would especially like to thank the PV owners who volunteered to take part in the measurement campaign. </p>
Fig. 1 in Opisthobranch mollusks (Gastropoda: Opisthobranchia) of cooling system of the Vladivostok Thermal Power Station 2
Fig. 1. Scheme of water-intake artificial inlet and water-pumping station (after Zvyagintzev [2005], with additions).
Рис. 2. А – сбросный канал (6 июнЯ 2008 г.); В – Cuthonella soboli (особь с поврежденными папиллами и ее кладка); C, D – Coryphella athodona (D – кладки); E–G – Catriona columbiana (F – кладка, G – радула). МасШтаб: B–F – 5 мм, G – 20 мкм. in Opisthobranch mollusks (Gastropoda: Opisthobranchia) of cooling system of the Vladivostok Thermal Power Station 2
Рис. 2. А – сбросный канал (6 июнЯ 2008 г.); В – Cuthonella soboli (особь с поврежденными папиллами и ее кладка); C, D – Coryphella athodona (D – кладки); E–G – Catriona columbiana (F – кладка, G – радула). МасШтаб: B–F – 5 мм, G – 20 мкм.
Fig. 2. A in Opisthobranch mollusks (Gastropoda: Opisthobranchia) of cooling system of the Vladivostok Thermal Power Station 2
Fig. 2. A – discharging canal (6th June, 2008); В – Cuthonella soboli (injured specimen and egg mass); C, D – Coryphella athodona (D – egg mass); E–G – Catriona columbiana (F – egg mass, G – radula). Scale bar: B–F – 5 mm, G – 20 μm.
Dataset: Ballard Power Systems Inc. (BLDP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Monolithic Power Systems, Inc. (MPWR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset for the power system modelling of the West African Power Pool (WAPP)
<p>Input datasets and simulation results for the West African Power Pool (WAPP) simulation with <a href="http://dispaset.eu/">Dispa-SET</a> described in <a href="https://ec.europa.eu/jrc/en/publication/analysis-water-power-nexus-west-african-power-pool">this technical report</a>. All the assumptions and the model are described in the report, the four files contain the input datasets for the "current" and "future" scenario and the simulation results.</p> <p>Full citation to the technical report:</p> <p>DE FELICE, M., GONZÁLEZ APARICIO, I., HULD, T., BUSCH, S., HIDALGO GONZÁLEZ, I.,<em> Analysis of the water-power nexus in the West African Power Pool - Water-Energy-Food-Ecosystems project</em>, EUR 29617 EN, Publications Office of the European Union, Luxembourg, 2019, ISBN 978-92-79-98138-8, doi:10.2760/362802, JRC115157</p>
Multi-Source Distributed System Data for AI-powered Analytics
<p><strong>Abstract:</strong></p> <p>In recent years there has been an increased interest in Artificial Intelligence for IT Operations (AIOps). This field utilizes monitoring data from IT systems, big data platforms, and machine learning to automate various operations and maintenance (O&M) tasks for distributed systems.<br> The major contributions have been materialized in the form of novel algorithms.<br> Typically, researchers took the challenge of exploring one specific type of observability data sources, such as application logs, metrics, and distributed traces, to create new algorithms.<br> Nonetheless, due to the low signal-to-noise ratio of monitoring data, there is a consensus that only the analysis of multi-source monitoring data will enable the development of useful algorithms that have better performance. <br> Unfortunately, existing datasets usually contain only a single source of data, often logs or metrics. This limits the possibilities for greater advances in AIOps research.<br> Thus, we generated high-quality multi-source data composed of distributed traces, application logs, and metrics from a complex distributed system. This paper provides detailed descriptions of the experiment, statistics of the data, and identifies how such data can be analyzed to support O&M tasks such as anomaly detection, root cause analysis, and remediation.</p> <p><strong>General Information:</strong></p> <p>This repository contains the simple scripts for data statistics, and link to the multi-source distributed system dataset.</p> <p>You may find details of this dataset from the original paper:</p> <p><em>Sasho Nedelkoski, Jasmin Bogatinovski, Ajay Kumar Mandapati, Soeren Becker, Jorge Cardoso, Odej Kao, "Multi-Source Distributed System Data for AI-powered Analytics". </em></p> <p><strong>If you use the data, implementation, or any details of the paper, please cite!</strong></p> <p> </p> <p>BIBTEX:</p> <p>_________________________________________</p> <pre>@inproceedings{nedelkoski2020multi, title={Multi-source Distributed System Data for AI-Powered Analytics}, author={Nedelkoski, Sasho and Bogatinovski, Jasmin and Mandapati, Ajay Kumar and Becker, Soeren and Cardoso, Jorge and Kao, Odej}, booktitle={European Conference on Service-Oriented and Cloud Computing}, pages={161--176}, year={2020}, organization={Springer} } </pre> <p>___________________________</p> <p>The multi-source/multimodal dataset is composed of distributed traces, application logs, and metrics produced from running a complex distributed system (Openstack). In addition, we also provide the workload and fault scripts together with the Rally report which can serve as ground truth. We provide two datasets, which differ on how the workload is executed. The <em><strong>sequential_data</strong> </em>is generated via executing workload of sequential user requests. The <strong><em>concurrent_data </em></strong>is generated via executing workload of concurrent user requests.</p> <p>The raw logs in both datasets contain the same files. If the user wants the logs filetered by time with respect to the two datasets, should refer to the timestamps at the metrics (they provide the time window). <strong>In addition, we suggest to use the provided aggregated time ranged logs for both datasets in CSV format.</strong></p> <p><strong><strong>Important:</strong> The logs and the metrics are synchronized with respect time and they are both recorded on CEST (central european standard time). The traces are on UTC (Coordinated Universal Time -2 hours). They should be synchronized if the user develops multimodal methods. Please read the IMPORTANT_experiment_start_end.txt file before working with the data.</strong></p> <p>Our GitHub repository with the code for the workloads and scripts for basic analysis can be found at: <a href="https://github.com/SashoNedelkoski/multi-source-observability-dataset/">https://github.com/SashoNedelkoski/multi-source-observability-dataset/</a></p>
Data input for the RegMex model experiment on the power system and flexible sector coupling
<p>This file provides the input data used in the power system flexibility model experiment performed within the RegMex project. Comprehensive information about the project can be found in the project report [Lechtenböhmer2018] (in German, see link in the file). In the experiment performed with the data documented here, three scenarios were considered, labelled "Import", "Decentralized" and "Offshore". This file contains the input for all scenarios. All further information on the model and scenario configuration is available from the project report. Many technology parameter have been derived as own assumptions within previous projects, relying on different sources. Details can be found in the cited PhD and masters theses. In the experiment, Germany was modelled with 18 regions reflecting the transmission grid operator zones (see map in the file).</p>
Dataset for the publication "The Use of Voltage Transformers for the Measurement of Power System Subharmonics in Compliance With International Standards"
<p>This is dataset for paper published:</p> <p>G. Crotti, G. D’Avanzo, P. S. Letizia and M. Luiso, "The Use of Voltage Transformers for the Measurement of Power System Subharmonics in Compliance With International Standards," in <em>IEEE Transactions on Instrumentation and Measurement</em>, vol. 71, pp. 1-12, 2022, Art no. 9005912, doi: 10.1109/TIM.2022.3204318.</p> <p> </p>
Dataset: Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System
<p>The dataset provided here is intended for publication - Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System.</p> <p>Direct use of our provided datasets is available from Zenodo, and the source code to generate the datasets is published in <a href="https://gitlab.com/dlr-ve/esy/open-brazilian-energy-data">Gitlab</a>. We describe the data collection process in detail and open source the code for data processing and analysis in our publication.</p> <p><br> The assembled dataset includes the following subcategories, as detailed in the methods section of our publication: i) geospatial data for Brazil, ii) aggregated grid network topology, iii) vRES potentials --- profile and installable generation capacity, iv) geographically installable capacity of biomass thermal plants, v) hydropower plants inflow, vi) existing and planned power generators with their capacity, vii) electricity load profile, viii) scenarios of sectoral energy demand and ix) cross-border electricity exchanges. This dataset is resolved geographically by Brazilian federal states, and time series data are resolved by hours, spanning 2012-2020.</p> <p>The dataset can be used as input to popular open energy system models such as PyPSA and any other modelling framework.</p> <p>We encourage you to contribute to improving the datasets.</p>
Dataset describing the vulnerability of the Nigerian Power system via a new voltage stability pointer
<p>The Nigerian power network (NGP), 28-bus 330 kV dynamic stability assessment using a new voltage stability pointer (NVSP). Different case studies were considered and presented on dynamic stability including the contingency analysis.</p> <p> </p>
Distributional employment challenges and opportunities of decarbonizing the US power system
<p>This dataset presents the results in the manuscript entitled "Distributional employment challenges and opportunities of decarbonizing the US power system". The Low Carbon Transition Employment Distribution (LoCaTED) model used to generate the results can be found on GitHub (<a href="https://github.com/judyjwxie/LoCaTED">https://github.com/judyjwxie/LoCaTED</a>). The suite of data files is based on the ReEDS 2022 Standard Scenarios and our employment calculation variations. Each file shows the job creation in the number of jobs disaggregated into the year, US state, technology, and economic sector (defined by the JEDI model). </p>
Supplementary Material to 'Exploring the power of data-driven models for groundwater system conceptualization: A case study of the Grazer Feld Aquifer, Austria'
<p>This folder contains the supplementary materials to reproduce the results, tables, and figures from the following publication submitted to the Hydrogeology Journal: </p> <p>Kokimova A., Collenteur, R.A. & Birk, S. Exploring the power of data-driven models for groundwater system conceptualization: A case study of the Grazer Feld Aquifer, Austria.</p>
Black Start Allocation for Power System Restoration Data
<p>Data sets used for black start allocation instances.</p>
Dispa-SET Output files for the JRC report "Power System Flexibility in a variable climate"
<p>Here you can find the model results of the <a href="https://doi.org/10.2760/75312">report</a>:</p> <pre><code>De Felice, M., Busch, S., Kanellopoulos, K., Kavvadias, K. and Hidalgo Gonzalez, I., Power system flexibility in a variable climate, EUR 30184 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-18183-5 (online), doi:10.2760/75312 (online), JRC120338. </code></pre> <p>This dataset contains both the raw GDX files generated by the GAMS (<www.gams.com>) optimiser for the <a href="http://www.dispaset.eu">Dispa-SET model</a>. Details on the output format and the names of the variables can be found in the Dispa-SET documentation. A markdown notebook in R (and the rendered PDF) contains an example on how to read the GDX files in R.</p> <p>We also include in this dataset a data frame saved in the <a href="https://parquet.apache.org/">Apache Parquet format</a> that can be read both <a href="http://arrow.apache.org/blog/2019/08/08/r-package-on-cran/">in R</a> and <a href="https://arrow.apache.org/docs/python/parquet.html">Python</a>.</p> <p>A description of the methodology and the data sources with the references can be found into the report.</p> <p><strong>Linked resources</strong></p> <ul> <li>Input files:<strong> </strong>https://zenodo.org/record/3775569#.XqqY3JpS-fc</li> <li>Source code for the figures: https://github.com/energy-modelling-toolkit/figures-JRC-report-power-system-and-climate-variability</li> </ul> <p><strong>Update</strong></p> <p>[29/06/2020] Updated new version of the Parquet file with the right data in the column `climate_year`</p>
Supplementary material for "A Partitioned Finite Element Method for power-preserving discretization of open systems of conservation laws"
<p>This archive contains supplementary material for the paper "A Partitioned Finite Element Method for power-preserving discretization of open systems of conservation laws", containing the source codes for the numerial results presented in the paper. An arXiv pre-print version of the paper is available <a href="https://arxiv.org/abs/1906.05965">here</a>.</p> <p>The following codes are provided:</p> <ul> <li> <p><code>codes/simulation1D_small.jl</code>: small amplitudes (linear) 1D simulation</p> </li> <li> <p><code>codes/simulation1D_large.jl</code>: large amplitudes (nonlinear) 1D simulation</p> </li> <li> <p><code>codes/simulation1D_analytical_gradient</code>: large amplitudes 1D simulation, but using an analytical nonlinear Hamiltonian gradient expression</p> </li> <li> <p><code>codes/simulation2D.jl</code>: large amplitudes (nonlinear) 2D simulation</p> </li> <li> <p><code>codes/convergence1D.jl</code>: convergence analysis of the 1D linear case</p> </li> <li> <p><code>codes/convergence2D.m</code>: convergence analysis of the 2D linear case</p> </li> </ul> <p>A GitHub with the codes and a few instructions on usage is available <a href="http://github.com/flavioluiz/PFEM-article-supplementary-material">here</a>.</p> <p><strong>Acknowledgements</strong></p> <p>This work has been performed in the frame of the Collaborative Research DFG and ANR project INFIDHEM, entitled "Interconnected of Infinite-Dimensional systems for Heterogeneous Media", nº ANR-16-CE92-0028. Further information is available <a href="http://websites.isae-supaero.fr/infidhem/the-project">here</a>.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for nuclear power in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span></span><span><span>nuclear power generation</span></span><span> <span>from</span><span> the open literature</span><span>. </span></span><span><span>Nuclear energy is the second-largest source of low-carbon generation, supplying 9% of global electricity</span><span>.</span></span><span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> </span><span><span>604</span></span><span><span> datapoints from </span></span><span><span>19</span></span><span><span> sources</span><span>.</span> </span></p> <p><span><span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span> <span>It is </span><span>designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span> Technoeconomic data on nuclear power was collected from websites, reports, academic articles and databases of national and international organisations.</p>
Dataset for "A low-cost and low-power continuous measurement system for atmospheric carbonyl sulfide concentration"
<p>The dataset used in the manuscript "A low-power continuous measurement system for atmospheric carbonyl sulfide concentration" by Kamezaki et al. The dataset contains COS concentrations at Tsukuba in Japan. Additionally, the data used in the paper are also included. Raw data are available upon request from the author.</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.