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107 results for “Energy Consumption”
EnerGAware monitored energy consumption data
<p>Energy consumption data acquired during the monitoring period, from the houses that were part of the EnerGAware pilot.</p> <p>The data discriminated by dwelling, but it is anonymized.</p>
Less is More: Exploiting the Standard Compiler Optimization Levels for Better Performance and Energy Consumption
<p>The data used to generate the graphs in Figures 2 and 3 of the paper "Less is More: Exploiting the Standard Compiler Optimization Levels for Better Performance and Energy Consumption" at SCOPES'18. The data includes power consumption, code size, and running time, indexed by an ID that identifies the combination of compiler options. Data is stored in a CSV file that identifies the specific benchmark, organized into a zip file that identifies the chip architecture.</p> <p>See README.txt for details.</p>
On the Relationship between Software Security and Energy Consumption - Dataset
<p>The dataset for estimating the Relationship between Software Security and Energy Consumption described in Siavvas, Miltiadis, Marantos, Charalampos, Papadopoulos, Lazaros, Kehagias, Dionysios, Soudris, Dimitrios, & Tzovaras, Dimitrios. (2019). On the Relationship between Software Security and Energy Consumption (Version 1.0).</p>
Energy Consumption Due To Test Quality: State of Affairs, Impacts, and Ways Forward
<p>This replication package contains data and scripts used in <strong>"Energy Consumption Due To Test Quality: State of Affairs, Impacts, and Ways Forward"</strong></p> <p>For details, please read README</p>
NB-IoT vs. LTE-M: Measurement Data of the Energy Consumption of LPWAN Technologies
<p><strong>NB-IoT vs. LTE-M: Measurement Data of the Energy Consumption of LPWAN Technologies</strong></p> <p>This dataset contains the raw energy measurements as well as R scripts to reproduce the energy consumption plot for the corresponding paper.</p> <p>Each .csv file contains a specific set of measurements and we provide a script to read, process and plot the contained data.</p> <p><strong>Figure 3</strong></p> <p>Mean energy consumption of the different phases for Authentication for NB-IoT and LTE-M.</p> <p>Due to the fact that the duration of <em>Idle Connected</em> in the measurement scripts was 30 seconds and 60 seconds for <em>Idle Not Connected</em>, the D-value and the mean power consumption are divided by 2.</p> <ul> <li>Data – energy_measurements_fig3.csv</li> <li>Code – fig3.R</li> </ul> <p><strong>Figure 4</strong></p> <p>Mean energy consumption of the different phases for Data Connection and Download for NB-IoT and LTE-M for 1KB of data in HTTP.</p> <p>The delay between the measurements for Figure 4 were all 30 seconds long, but the identified <em>Standby</em> and <em>Idle</em> phases have different lengths. Therefore, the <em>Idle</em> phase values for both access technologies have been normalized and calculated for 20 seconds each.</p> <ul> <li>Data – energy_measurements_fig4.csv</li> <li>Code – fig4.R</li> </ul> <p><strong>Figure 5</strong></p> <p>Mean energy consumption of the different phases for Data Connection and Download for HTTP and MQTT for 1KB of data in NB-IoT.</p> <p>In this scenario the delay between the measurements were different again. For <em>MQTT</em> the delay was 150 seconds and for <em>HTTP</em> 30 seconds. Therefore, the data during the <em>Idle</em> and <em>Standby</em> (only for <em>MQTT</em>) phase is normalized and calculated for 20 seconds and 10 seconds, respectively. During the <em>MQTT</em> <em>Idle</em> phase measurements, the device disconnects. This is not taken into account for the evaluation, which is why these energy values are discarded for this figure.</p> <ul> <li>Data – energy_measurements_fig5.csv</li> <li>Code – fig5.R</li> </ul> <p><strong>Contact</strong></p> <p>For questions or issues with this code, please contact Viktoria Vomhoff (viktoria.vomhoff@uni-wuerzburg.de) or any of the authors of the related publication.</p>
Energy consumption data of office building and energy production data of a 185KW PV plant
<p>The dataset includes two-year monitoring data from the energy consumption of and office building located in center of Italy. The building has HVAC system, heat pumps for space heating /cooling (overall 120-140 KW load) and lighting subsystems controlled individually and/or overall by BMS.</p> <p>The building is a part of a small smart-grid which includes a PV plant (180KW). Energy production data are monitored and a two-year dataset is provided as well. </p>
LOFAR Carbon Footprint and Energy Consumption
<p>The LOw Frequency ARray (LOFAR) is a European radio telescope operating since 2010 in the frequency bands 10 - 80 MHz and 110 - 250 MHz. This Excel model provides an analysis of the energy consumption and the carbon footprint of LOFAR. The analysis uses a Life Cycle Analysis following the Green House Gas protocol. Results include the footprint stemming from operations of all LOFAR stations and central processing. The impact of a number of typical science projects is analyzed as well. This model provides a transparent baseline to the sustainability of LOFAR and can serve as a blueprint for the analysis of other research infrastructures.</p>
Energy Saving from Reduced building Consumption in Valladolid city
<p>Climate change can cause overheating in city centers, especially through the “heat island effect”. Green urban infrastructure can play a role in climate change adaptation through reducing air and surface temperature by providing shading and enhancing evapo-transpiration, which leads to energy and carbon savings from reduced building energy consumption especially in summer. On the other hand, insulating effect of plants reduces heating energy consumption and associated carbon emissions in winter. </p> <p>This indicator was calculated for the UrbanGreenUP monitoring program. </p>
The Energy consumption and Carbon Footprint of the LOFAR Telescope V2.0
<p>The LOw Frequency ARray (LOFAR) is a European radio telescope operating since 2010 in the frequency bands 10 - 80 MHz and 110 - 250 MHz. This article provides an analysis of the energy consumption and the carbon footprint of LOFAR. The approach used is a Life Cycle Analysis (LCA). We find that one year of LOFAR operations requires 3,627 MWh of electricity, 48,714 m3 gas and 135,497 liters of fuel. The associated carbon emission is 2,624 tCO2e/year. Results include the footprint stemming from operations of all LOFAR stations and central processing, but exclude scientific post-processing and activities. The potential recovery of embodied footprint in construction materials at the end of life equals 17%. The electrical energy required for scientific processing is assessed separately. It ranges from 1% (standard The Energy Consumption and Carbon Footprint of the LOFAR Telescope imaging and time-domain), to 40% (wide field long baseline imaging) of the energy consumption for the observation. The outcome provides<br> a transparent baseline in making LOFAR more sustainable and can serve as a blueprint for the analysis of other research infrastructures.</p>
Milling process energy consumption
<p>This dataset is about a CNC machine performing milling processes<br>Each observation means one particular milling process with the machine</p> <p>Axis - the machine's cutting tool was moving along axis X/Y/Z<br>Feed [mm/min] - the cutting tool moved at this speed<br>Path [mm] - the cutting tool moved this distance</p> <p>Energy requirement [kJ] - energy consumption of the process </p>
Optimal Control of Renewable Energy Communities with Controllable Assets: consumption and production profiles
<p>consumption and production profiles for cases I and II used for computing simulations in Optimal Control of Renewable Energy Communities with Controllable Assets</p>
Energy consumption, execution time and fail requests rate of a proactive energy-aware auto-scaling solution for edge-based infrastructures applied to real-world workload.
<p>Spreadsheet of the results obtained with our horizontal auto-scaling proposal presented in "A proactive energy-aware auto-scaling solution for edge-based infrastructures". In that research, we present a proactive horizontal auto-scaling framework for edge infrastructures, which considers both the base (idle) and dynamic (due to application execution) energy consumption of edge nodes and the node scaling mechanism. Simulations were performed with the EdgeCloudSim simulator with a workload provided by Shanghai Telecom and the results show up to a 92.5% decrease in energy consumption, a failed request rate of up to 0%, and reasonable execution times of the auto-scaling process for different problem sizes.</p> <p>Proactive auto-scaling mechanisms in edge-based infrastructures can anticipate user service requests by allocating computing resources while supporting the quality of service needed by a vast range of applications requiring, e.g., a low latency or response time. </p> <p>This work is supported by the European Union's H2020 research and innovation program under grant agreement DAEMON 101017109 and by the projects co-financed by FEDER funds LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 (MCI/AEI) and RHEA P18-FR-1081.</p>
Artifact of the paper: Light-weight prediction for improving energy consumption in HPC platforms
<p>Please refer to the <a href="../records/11208389/files/artifact-overview.pdf?download=1&preview=1" target="_blank" rel="noopener">artifact-overview.pdf</a> file in this dataset for instructions to reproduce the experiments we have conducted for this article, or for more context about the article.</p>
Raw Data for Energy Consumption Comparison of DMA-Based and FatFs Storage Systems on Wearable Devices
<p>This dataset contains raw oscilloscope measurements comparing the energy consumption of a Direct Memory Access (DMA)-based storage system versus the FatFs file system for wearable devices. The data was collected as part of the study "Direct Memory Access-Based Data Storage for Long-Term Acquisition Using Wearables in an Energy-Efficient Manner".</p> <p>The dataset includes voltage drop measurements across a 2-ohm shunt resistor, captured using an Analog Discovery 2 digital oscilloscope at a 500 kHz sampling rate. Measurements were taken under various conditions:</p> <ul> <li>Storage systems: DMA-based (proposed) and FatFs</li> <li>SD card capacities: 4 GB and 8 GB</li> <li>Write frequencies: 2 Hz and 5 Hz (referring to the frequency of writing a specific data block of 15,872 bytes)</li> <li>With and without a smoothing capacitor</li> </ul> <p>Each CSV file contains 20 million samples, equivalent to 40 seconds of data acquisition. File names encode the experimental conditions, including the storage system, write frequency, number of samples, acquisition rate, acquisition time, data format, SD card size, and absence of the smoothing capacitor.</p> <p>The data is organized into two main folders:</p> <ol> <li>"cap": Contains measurements with the smoothing capacitor</li> <li>"no_cap": Contains measurements without the smoothing capacitor</li> </ol> <p>This raw data can be used to reproduce the energy consumption and write speed analyses presented in the article, as well as for further investigation into the performance of embedded storage systems for wearable devices.</p>
Energy Consumption of IO APIS (ESEM'2019 paper)
<p>## About the experiments</p> <p>### Acronyms used in the figures</p> <p>- BufferedReader (BR).<br> - LineNumberReader (LNR).<br> - CharArrayReader (CAR).<br> - PushbackReader (PBR).<br> - FileReader (FR).<br> - FileInputStream (FIS).<br> - BufferedInputStream (BIS).<br> - StringReader (SR).<br> - PushbackInputStream (PBIS).<br> - StringBufferInputStream (SBIS).<br> - ByteArrayInputStream (BAIS).<br> - LineNumberInputStream (LNIS).<br> - Scanner (SCN).<br> - O método readAllLines da classe Files, e um Stream de String (RFAL).<br> - O método lines da classe Files, e um Stream de String (RFL).<br> - O método newBufferedReader da classe Files, e um Stream de String (BRFL)<br> - BufferedWriter (BW).<br> - FileWriter (FW).<br> - StringWriter (SW).<br> - PrintWriter (PW).<br> - CharArrayWriter (CAW).<br> - FileOutputStream (FOS).<br> - ByteArrayOutputStream (BAOS).<br> - BufferedOutputStream (BOS).<br> - PrintStream (POS).</p> <p>### Settings</p> <p>For each setting, we experimented with three files of different sizes: 1 Mb, 10 Mb e 20 Mb.</p> <p>The experiments were ran in the following machine</p> <p>- Hardware: Intel® Core™ i7-2670QM CPU @ 2.20GHz, with 4 processors, 16GB DDR3 1600MHz, Ubuntu 16.04 LTS (kernel 4.4.0-112-generic).<br> - Java: Java(TM) SE Runtime Environment, version 1.8.0-151</p>
SPARCS_WP3_Espoo_City_Energy consumption in Espoo, Finland
<p>Energy consumption in Espoo, Finland, divided by sector. Provided by the Helsinki Region Environmental Authority. 2000-2022</p>
Machine Learning for Energy Consumption Prediction of Numerical Controlled Programs - NC Files
<p>The NC files housed within this DOI represent the Data the Machine Learning Models were Trained/Validated/Tested on, during the execution of the work in the thesis, " Machine Learning for Energy Consumption Prediction of Numerically Controlled Programs." Theses files were created by Samuel D. Stencel, a Gradute Research Assistant at Purdue University.</p> <p> </p>
Energy consumption of 15 electric vehicles (one day resolution)
<p><strong>Energy consumption of 15 electric vehicles (one day resolution)</strong></p> <p>Sérgio Ramos, João Soares, Zahra Foroozandeh, Inês Tavares, Zita Vale</p> <p><strong>Paper title: TODO</strong></p> <p>Type: EV consumption</p> <p>Duration: One year</p> <p>Resolution: One day</p> <p>Application: Paper submitted on</p> <p>Sheets description:</p> <ul> <li>EV 1-15: Contains the information of the energy consumption and initial State of Charge of each EV (kWh).</li> </ul>
Energy consumption and PV generation data of 15 prosumers (15 minute resolution)
<p><strong>Energy consumption and PV generation data of 15 prosumers (15 minute resolution)</strong></p> <p>Sérgio Ramos, João Soares, Zahra Foroozandeh, Inês Tavares, Zita Vale</p> <p><strong>Paper title: (All papers)</strong></p> <p>Type: Energy consumption and PV generation data</p> <p>Duration: Year 2019 (15 minute – 35 040 periods)</p> <p>Resolution: 15 minutes</p> <p>Application: Paper submitted on</p> <p>Sheets description:</p> <ul> <li>Total PV production: Contains the generation of the PV panels;</li> <li>Common services: Contains information of the energy consumption of the common services of the building;</li> <li>Consumer 1-15: Contains the information of the energy consumption of each consumer.</li> </ul>
Data files for "Estimating a Social Cost of Carbon for Global Energy Consumption"
<p>Data files for "Estimating a Social Cost of Carbon for Global Energy Consumption".</p> <p>Findings of the paper can be replicated using these data files, along with code at https://github.com/ClimateImpactLab/energy-code-release-2020/.</p> <p> </p>
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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.