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23 results for “Power consumption”
Brazilian Ministries' Esplanade Power Consumption
<p>Electric and climatic dataset of one building of Ministries' Esplanade (headquarter of the Brazilian Executive Government), from 2018-02-01 to 2019-12-13.</p> <p>There are 84926 samples, each for 10 minutes of electrical and climatic measurements, described by 12 variables.</p>
Real Power Consumption Traces
<p>We collected 5 real power consumption traces and we used one to generate the training dataset for the LSTM. The other four real power consumption traces were used for the testing dataset.</p> <p>Each real power consumption trace consists of a pair of lists:</p> <ul> <li>traces: a list of samples, which are the measurements of power consumption</li> <li>cycle: a list of element pairs, where each pair consists of: <ul> <li>a numeric tag identifying the field operation performed (Addition, Subtraction, Multiplication, Square and other routine operations not of interest to us):</li> <li>the clock ticks counter when the field operation starts</li> </ul> </li> </ul>
BIKE Key-Recovery: Combining Power Consumption Analysis and Information-Set Decoding
<p>Data used in the paper: "BIKE Key-Recovery: Combining Power Consumption Analysis and Information-Set Decoding". The paper has been accepted at <a href="https://sulab-sever.u-aizu.ac.jp/ACNS2023/">ACNS-2023</a>.</p> <p>The available dataset contains a file with a few power consumption curves taken from a Cortex-M4 (STM32F4) on a CW308 board.</p> <p>The file is a numpy array stored using the np.save API.<br> The file can be directly used for running the notebooks provided in the <a href="https://github.com/benoitgerard/sca-bike">publication github</a>.</p>
Cyber-Physical System power Consumption
<h1>Files</h1> <p>This dataset is comprised of 5 CSV files contained in the data.zip archive. Each one represents a production machine from which various sensor data has been collected. The average cadence for collection was 5 measurements per second. The monitored devices where used for hydroforming.</p> <p>The collection period covered the period from 2023-06-01 until 2023-08-05.</p> <h2>Data</h2> <p>These files represent a complete data dump from the data available in the time-series database, InfluxDB, used for collection. Because of this some columns have no semantic value for detecting production cycles or any other analytics.</p> <p>Each file contains a total of 14 columns. Some of the columns are artefacts of the query used to extract the data from InfluxDB and can be discarded. These columns are: results, table _start, _stop</p> <ul> <li><em>results</em> - An artefact of the InfluxDB query, signifies postprocessing of results in this dataset. It is "mean".</li> <li><em>table</em> - An artefact of the InfluxDB query, can be discarded.</li> <li><em>_start</em> and <em>_stop</em> - Refers to ingestion related data, used in monitoring ingestion. </li> <li><em>_field</em> - An artefact of the InfluxDB query, specifying what field to use for the query.</li> <li><em>_measurement</em> - An artefact of the InfluxDB query, specifying what measurement to use for the query. Contains the same information as device_id.</li> <li><em>host</em> - An artefact of the InfluxDB query, the unique name of the host used for the InfluxDB sink in Kubernetes.</li> <li><em>kafka_topic</em> - Name of the Kafka topic used for collection.</li> </ul> <p> </p> <p>Pertinent columns are:</p> <ul> <li><strong><em>_time</em></strong> - Denotes the time at which a particular event has been measured, it is used as index when creating a dataframe.</li> <li><em><strong>_time.1</strong></em> - Duplicate of _time for sanity check and ease of analysis when _time is set as index</li> <li><em><strong>_value</strong></em> - Represents the value measured by each sensor type.</li> <li><em><strong>device_id </strong></em>- Unique identifier of the manufacturing device, should be the same as the file name, i.e. B827EB8D8E0C.</li> <li><em><strong>ingestion_time</strong></em> - Timestamp when the data has been collected and ingested by influxDB.</li> <li><em><strong>sid</strong></em> - Unique sensor ID; the power measurements can be found at sid 1.</li> </ul> <p> </p> <h1>Annotations</h1> <p>There are two additional files which contain annotation data: </p> <ul> <li><em><strong>scamp_devices.csv</strong></em> - Contains mapping information between the dataset device ID (defined in column "<em>DeviceIDMonitoring</em>") and the ground truth file ID (defined in column "<em>DeviceID</em>")</li> <li><em><strong>scamp_report_3m.csv </strong></em>- Contains the ground truth, which can be used for validation of cycle detection and analysis methods. The columns are as follows: <ul> <li><strong><em>ReportID</em></strong> - Internal unique ID created during data collection. It can be discarded.</li> <li><em><strong>JobID</strong></em> - Internal Scheduling Job unique ID.</li> <li><em><strong>DeviceID</strong></em> - The unique ID of the devices used for manufacturing needs to be mapped using the <em>scamp_device.csv</em> data.</li> <li><em><strong>StartTime</strong></em> - Start time of operations</li> <li><em><strong>EndTime</strong></em> - End time of operations</li> <li><em><strong>ProductID</strong></em> - Unique identifier of the product being manufactured.</li> <li><em><strong>CycleTime</strong></em> - Average length of cycle in seconds, added manually by operators. It can be unreliable.</li> <li><em><strong>QuantityProduced</strong></em> - Number of products manufactured during the timeframe given by <em>StartTime</em> and <em>EndTime</em>.</li> <li><em><strong>QuantityScrap</strong></em> - Number of scraped/malformed products in the given timeframe. These are part of the <em>QuantityProduced</em><em>,</em><strong> </strong>not in addition to it.</li> <li><em><strong>IntreruptionMinuted</strong></em> - Minutes of production halt.</li> </ul> </li> <li><em><strong>scamp_patterns.csv</strong></em> - Contains the start and end timestamp for selected example production cycles. These where chosen based on expert users.</li> </ul> <h1>Jupyter Notebook</h1> <p>We have provided a sample Jupyter notebook (<em>verify_data.ipynb</em>), which gives examples of how the dataset can be loaded and visualised as well as examples of how the sample patterns and ground truth can be addressed and visualised.</p> <h2>Note</h2> <p>The Jupyter Notebook contains an example of how the data can be loaded and visualised. Please note that both data should be filtered based on sid; the power measurements are collected by sid 1. See Notebook for example.</p>
Randomly sampled coefficients for synchrotron radiative transfer in the Stokes basis, power law model, computed by rimphony, for consumption by neurosynchro
<p>This directory contains a training set of 22 million randomly-sampled radiative transfer coefficients generated by <a href="https://github.com/pkgw/rimphony/">rimphony</a>, suitable for use with the <a href="https://github.com/pkgw/neurosynchro/">neurosynchro</a> package. These coefficients can be used for numerical radiative transfer of synchrotron emission in the Stokes basis with a package such as <a href="https://github.com/jadexter/grtrans/">grtrans</a>.</p> <p>In this particular dataset, coefficients were computed using a model of a power law electron distribution isotropic in pitch angle. The input parameters, which were sampled randomly in a three-dimensional space, are:</p> <ul> <li><em>s</em>, the harmonic number, dimensionless, sampled logarithmically between 5 and 50,000,000.</li> <li><em>theta</em>, the angle between the ray path and the local magnetic field, measured in radians, sampled linearly between 0.001 and π/2 (namely, 1.5707963267948966).</li> <li><em>p</em>, the power-law index of the energetic electrons, dimensionless, sampled linearly between 1.5 and 7.</li> </ul> <p>The coefficients were computed on Harvard’s Odyssey cluster using Git commit <a href="https://github.com/pkgw/rimphony/commit/772161ebda0217b8c1ccb8ce3801ad9dc3701a4f">772161</a> of rimphony. A total of about 5,000 CPU hours were used, with 500 processes running for about 10 hours each. There are 2,748,835 data rows in total. The data are provided in their original format, split among 500 files, so that smaller subsamples of the data may be loaded easily. A README.md file provides more detailed information.</p>
Trained neural network data for synchrotron radiative transfer in the Stokes basis, power law model, computed by rimphony, for consumption by neurosynchro
<p>This archive contains data representing a trained-up neural network suitable for use with the <a href="https://github.com/pkgw/neurosynchro/">neurosynchro</a> package. The network generates coefficients that can be used for numerical radiative transfer of synchrotron emission in the Stokes basis with a package such as <a href="https://github.com/jadexter/grtrans/">grtrans</a>.</p> <p>In this particular dataset, networks were trained on a training set of coefficients generated by <a href="https://github.com/pkgw/rimphony/">rimphony</a> that is available as <a href="https://doi.org/10.5281/zenodo.1341154">DOI:10.5281/zenodo.1341154</a>. The data were generated using a model of a power law electron distribution isotropic in pitch angle. The input parameters, which were sampled randomly in a three-dimensional space, were:</p> <ul> <li><em>s</em>, the harmonic number, dimensionless, sampled logarithmically between 5 and 50,000,000.</li> <li><em>theta</em>, the angle between the ray path and the local magnetic field, measured in radians, sampled linearly between 0.001 and π/2 (namely, 1.5707963267948966).</li> <li><em>p</em>, the power-law index of the energetic electrons, dimensionless, sampled linearly between 1.5 and 7.</li> </ul> <p>The training set was computed on Harvard’s Odyssey cluster using Git commit <a href="https://github.com/pkgw/rimphony/commit/772161ebda0217b8c1ccb8ce3801ad9dc3701a4f">772161</a> of rimphony. A total of about 5,000 CPU hours were used, with 500 processes running for about 10 hours each, yielding about 22 million numbers. Training the networks took about 3 hours on an 8-core laptop.</p> <p>For the purposes of <em>neurosynchro</em>, the formats of the files in this package should be regarded as internal implementation details. The <a href="https://pypi.org/project/neurosynchro/">neurosynchro</a> Python package will load up the files in this archive and use them to predict synchrotron coefficients. For specifics, see <a href="https://neurosynchro.readthedocs.io/en/stable/">the neurosynchro documentation</a>.</p>
Shifts Marine Cargo Vessel Power Consumption Prediction Dataset
<p>This archive contains the data for the Shifts Benchmark on cargo vessel power consumption prediction. This dataset is provided by the Shifts Project to enable assessment of the robustness of models to distributional shift and the quality of their uncertainty estimates. A full description of the benchmark is available in https://arxiv.org/pdf/2206.15407. To find out more about the Shifts Project, please visit https://shifts.ai . </p> <p> </p> <p> </p>
InSecTT WSN Power Consumption Dataset
<p>This dataset consists of power consumption measurements of four different Wireless Sensor Network (WSN) protocols. We consider Bluetooth Low Energy (BLE), Thread, the EPhESOS protocol, and UWB in this dataset. We focus on a wireless sensor node use case, i.e. a sensor node gathers every 100 ms 2 bytes of data and transmits it to the network. We compare the energy consumption of the different network protocols and also investigate how much the power consumption decreases if more data is transmitted less frequently. Since the energy consumption is measured in 10 µs resolution, this dataset can also be used to evaluate the influence of the different parts of the communication.</p> <p>This dataset is a work from <a href="https://silicon-austria-labs.com/">Silicon Austria Labs GmbH</a> (SAL), the <a href="https://www.lcm.at/">Linz Center of Mechatronics</a> (LCM), and the <a href="https://www.jku.at/en/institute-for-communications-engineering-and-rf-systems/">Institute for Communications Engineering and RF-Systems</a> (NTHFS) of the Johannes Kepler University (JKU) in Linz for the <a href="https://www.insectt.eu/">InSecTT project</a>.</p>
Synthetic Power Consumption Traces
<p>Dataset composed of (more than) 10,000 synthetic power consumption traces, used to generate the input dataset for both training and testing the LSTM network. The traces are saved via the <em>NumPy</em> library in a <code>.npz</code> file format: each file contains 500 synthetic power consumption traces.</p>
Dataset from Paper: What does Power Consumption Behavior of HPC Jobs Reveal?
<p>The dataset in the tarball was used as job- and power-trace input for the paper "What does Power Consumption Behavior of HPC Jobs Reveal?", published at the International Parallel and Distributed Processing Symposium 2020 (IPDPS'20) in New Orleans, Louisiana.</p> <p>For more details on files, clusters, and data format, please see the README file in the archive.</p>
Current consumption for different LoRaModules and output powers
<p>This dataset contains the measured input power for a given transmission power at 1.8V and 3.3V for different LoRaModules, including: SX1276 (RFM95, PA Boost Configuration), SX1276 (inAir9), SX1262 (DevBoard), SX1261 (custom Board).</p>
Predicting Performance and Power Consumption of Parallel Applications
<p><em><strong>Abstract: </strong>Current architectures provide many control knobs for the reduction of power consumption of applications, like reducing the number of used cores or scaling down their frequency. However, choosing the right values for these knobs in order to satisfy requirements on performance and/or power consumption is a complex task and trying all the possible combinations of these values is an unfeasible solution since it would require too much time. For this reasons, there is the need for techniques that allow an accurate estimation of the performance and power consumption of an application when a specific configuration of the control knobs values is used. Usually, this is done by executing the application with different configurations and by using these information to predict its behaviour when the values of the knobs are changed. However, since this is a time consuming process, we would like to execute the application in the fewest number of configurations possible. In this work, we consider as control knobs the number of cores used by the application and the frequency of these cores. We show that on most Parsec benchmark programs, by executing the application in 1% of the total possible configurations and by applying a multiple linear regression model we are able to achieve an average accuracy of 96% in predicting its execution time and power consumption in all the other possible knobs combinations.</em></p> <p>This dataset includes the raw data of the experiments as well as the scripts used to plot them.</p>
Tour de France data for the improvement of energy consumption in devices powered by limited energy sources
<p>We propose a set of data that were collected as part of a "tour de France" with electrical wheelchair.</p> <p>Part of these data are allowed to propose a mathematical model based on an experimental methodology on the energy consumed in smartphones.</p> <p>The objective is to make accessible the data related to the publications in several fields of research (computer science, telecommunication, meteorological science, artificial intelligence, statistics ...)</p>
PM100: A Job Power Consumption Dataset of a Large-Scale HPC System
<p>The dataset is a collection of jobs extracted from the job_table data of M100 (<a href="https://doi.org/10.5281/zenodo.7588815">https://doi.org/10.5281/zenodo.7588815</a>), a collection of data extracted from a Tier-0 supercomputer hosted at CINECA (Marconi100, <a href="https://www.hpc.cineca.it/hardware/marconi100">https://www.hpc.cineca.it/hardware/marconi100</a>). The original job data present in M100 are filtered out by considering only the jobs running exclusively on the resources. Each job entry included in PM100 contains the power consumption of the job recorded at Node level, CPU level and Memory level. The final dataset contains 231116 jobs, executed on Marconi100 between May and October 2020. </p><p>The dataset is stored as a parquet file, where each entry contains the information on a job execution. </p><p>The structure of the data, as well as the code to generate them, is contained in the official GitHub repository of the project: <a href="https://github.com/francescoantici/PM100-data/">https://github.com/francescoantici/PM100-data/</a>.</p>
Dataset of bootup power consumption traces for four networking equipments.
<p>This dataset contains a collection of four NumPy files (.npy) that contain around 500 examples of power consumption. These power consumptions corresponds to the bootup sequences for four different networking equipment (two routers and two switches).</p>
Improvement of the Power Consumption of Docker Container Environments by evaluation and adaptation of logging techniques
<pre>Container technologies are becoming increasingly important in cloud computing, data centers, and software development. As a result, the number of computing units increases significantly, leading to an increase in power consumption. If the energy consumption of a computing instance is changed minimally by an adapted configuration, this can have a large impact on entire container environments. This paper uses the example of docker logging drivers to show what these effects could be in terms of CPU consumption, reachability, power consumption and power costs.</pre>
Household Active Power Consumption Dataset
<p>This dataset is part of the Monash, UEA & UCR time series regression repository. <a href="http://tseregression.org/">http://tseregression.org/</a></p> <p>The goal of this dataset is to predict total active power consumption in a household. This dataset contains 1440 time series obtained from the Individual household electric power consumption dataset from the UCI repository. The time series has 5 dimensions. This includes measurements for voltage, current annd 3 sub-metering energy usage.</p> <p> <br> Please refer to <a href="https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption">https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption</a> for more details<br> <br> Source<br> Georges Hebrail (georges.hebrail '@' edf.fr), Senior Researcher, EDF R&D, Clamart, France<br> Alice Berard, TELECOM ParisTech Master of Engineering Internship at EDF R&D, Clamart, France</p>
Household Reactive Power Consumption Dataset
<p>This dataset is part of the Monash, UEA & UCR time series regression repository. <a href="http://tseregression.org/">http://tseregression.org/</a></p> <p>The goal of this dataset is to predict total reactive power consumption in a household. This dataset contains 1440 time series obtained from the Individual household electric power consumption dataset from the UCI repository. The time series has 5 dimensions. This includes measurements for voltage, current annd 3 sub-metering energy usage.</p> <p> <br> Please refer to <a href="https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption">https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption</a> for more details<br> <br> Source<br> Georges Hebrail (georges.hebrail '@' edf.fr), Senior Researcher, EDF R&D, Clamart, France<br> Alice Berard, TELECOM ParisTech Master of Engineering Internship at EDF R&D, Clamart, France</p>
Power consumption per bit of short distance wireless transmission
<p>Survey results on radio transmitter power consumption per bit reported in 2012 [1] and 2018 [2], and that of 100GbE as a reference. </p> <p>[1] T. Kawanishi, THz and Photonic Seamless Communications, IEEE/OSA J. Lightwave Technol. 37, 1671-1679 (2019)</p> <p>[2] T. Kawanishi, Optical signal processing for millimeter-wave wireless systems, 2012 IEEE Photonics Society Summer Topical Meeting Series, Seattle, WA, 2012.</p>
Data set for reliability-based lift-to-power consumption optimization with an accelerated Kriging model for clapping-wing micro air vehicles
<p>Procedures of the reliability-based lift-to-power consumption optimization with an accelerated Kriging model</p> <p>Step 1: Run the file “LHS.m” to generate initial samples.</p> <p>Step 2: Modify the aerodynamic model according to initial samples (e.g. flapping1_Def.xml, flapping1.bat), and then run the “.bat file” to obtain the original force data.</p> <p>Step 3: Run the file “Kriging.m” to obtain the average lift using a filter.</p> <p>Step 4: Run the file “FW_2.m”, “FW_3.m” to obtain sub-optimal-result.</p> <p>Step 5: Find the new training sample and obtain the eigenvalue of the new training sample.</p> <p>Step 6: Rerun the file “FW_2.m”, “FW_3.m” to obtain sub-optimal-result by reloading the new “.mat” files (e.g. FW_2_41.mat, FW_2_P_20.mat).</p> <p>Step 7: Go to Step 4 until the convergence criteria are satisfied.</p> <p>Step 8: Obtain the optimal result. PS: Other files are function files.</p>
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