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107 results for “Energy Consumption”
A prototyping software engineering approach for designing and implementing model-based cloud mobile application for rationalized energy consumption
<p>The dataset used in this study comprises four files containing household consumers' energy consumption records. These records are collected at hourly intervals, providing detailed information on the energy usage patterns of the households. The dataset serves as a valuable resource for analyzing energy consumption trends, developing energy management strategies, and exploring the potential for energy efficiency improvements in residential settings.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Can industrial transfer lead to carbon transfer in China's high energy consumption industry?
<p>The above is the data for all images in the manuscript, mainly including: Carbon emissions and carbon transfer of energy-intensive industries in eight regions、Path of energy-intensive industrial transfer and carbon transfer in 2002, 2007, 2012 and 2017、MGWR regression results in 2002, 2007, 2012 and 2017</p>
Daylight-controlled Lighting Adjusted for Geographical Orientation : Effects on Recovery, Energy Consumption and User Satisfaction
ClinicalTrials.gov study NCT05868291. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Evaluation of Energy Drink Consumption on ECG and Hemodynamic Parameters in Young Healthy Volunteers
ClinicalTrials.gov study NCT02023723. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effects of Beef Protein Consumption on Energy Intake
ClinicalTrials.gov study NCT01646749. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Energy Consumption and Cardiorespiratory Load During Walking With and Without Robot-Assistance
ClinicalTrials.gov study NCT02680496. IPD Sharing: Not stated. Countries: 1. Publications: 23.
The Effect of Energy Drink Consumption on Alcohol-Substance Use and It's Relationship With Impulsivity In University Students
ClinicalTrials.gov study NCT02593617. IPD Sharing: Not stated. Countries: 1. Publications: 4.
The Effect of Snack Consumption on Energy Intake in Preschoolers
ClinicalTrials.gov study NCT02207049. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Potato Consumption and Energy Balance
ClinicalTrials.gov study NCT03518515. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Health Effect of Energy Drink Consumption
ClinicalTrials.gov study NCT04961086. IPD Sharing: NO. Countries: 1. Publications: 6.
Energy consumption and greenhouse gas emissions data of activated carbon production using different biomass
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The effect of renewable and nuclear energy consumption on decoupling economic growth from CO2 emissions in Spain
<p>This study examines the relationship between renewable and nuclear energy consumption, carbon dioxide emissions and economic growth by using the Granger causality and non-linear impulse response function in a business cycle in Spain. We estimate the threshold vector autoregression (TVAR) model on the basis of annual data from the period 1970‒2018, which are disaggregated into quarterly data. Our analysis reveals that economic growth and CO<sub>2</sub> emissions are positively correlated during expansions but not during recessions. Moreover, we find that rising nuclear energy consumption leads to decreased CO<sub>2</sub> emissions during expansions, while the impact of increasing renewable energy consumption on emissions is negative but insignificant. In addition, there is a positive feedback between nuclear energy consumption and economic growth, but unidirectional positive causality running from renewable energy consumption to economic growth in upturns. Our findings do indicate that both nuclear and renewable energy consumption contribute to a reduction in emissions; however, the rise in economic activity, leading to a greater increase in emissions, offsets this positive impact of green energy. Therefore, a decoupling of economic growth from CO<sub>2</sub> emissions is not observed. These results demand some crucial changes in legislation targeted at reducing emissions, as green energy alone is insufficient to reach this goal.</p>
Characterizing Energy Consumption of Third-Party API Libraries using API Utilization Profiles (ESEM'2020 Dataset)
<p><strong>Motivation</strong></p> <p>This repository contains the data-set used as a basis for our ESEM'2020 paper <em>Characterizing Energy Consumption of Third-Party API Libraries using API Utilization Profiles</em> The dataset is comprised of 2 test scenarios: The first one is based on the paper from Rocha et al. (2019), the second one is based on the commonly known Google Gson library.</p> <p><strong>Description of the dataset</strong></p> <p>The dataset is stored in a file *.csv and contains the following data:</p> <ul> <li>id - an individual identifier</li> <li>name - the name of the library examined</li> <li>className - the class name as an abbreviation</li> <li>method - the name of the executed method</li> <li>duration - duration of method execution</li> <li>energyConsumption - computed energy consumption</li> <li>watts - recorded wattage</li> <li>uApi - the computed uAPI profile value</li> </ul> <p>Besides the data, this repository also contains the result images from the ESEM'2020 paper in pdf-file format.</p> <p><em><strong>Android I/O Experiment</strong></em></p> <p>For the Android I/O Experiment we examined the correlation between API utilization and energy consumption. For the dataset, we took inspiration from Rocha et al. (2019). The dataset uses abbreviations for the examined classes which are further described in the <em>readme.md</em> file</p> <ul> <li>Filename: io_test_052020.csv</li> </ul> <p><em><strong>Google Gson Experiment</strong></em></p> <p>The Google Gson dataset contains recordings for Google Gson version 2.8.5. Again, the dataset was used as a foundation for examining the correlation between the computed API profiles and energy consumption.</p> <ul> <li>Filename: gson_test_052020.csv</li> </ul> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p>
CONSUMPTION MANAGEMENT OF FUEL AND ENERGY RESOURCES - THE BASIS OF ENERGY EFFICIENCY
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Energy consumption and weather data collected continuously during a period of 29 months at Challenger building (France).
<p>The dataset consists of energy consumption and weather data collected continuously during a period of 29 months at Challenger building (France).</p> <p>Energy consumption data at 10 minutes intervals include:</p> <ul> <li>Heating and cooling electrical consumption (indoor and outdoor comfort units) for the 8 zones in the South Triangle (kWh)</li> <li>Lighting consumption for the 8 zones in the South Triangle (kWh)</li> <li>Plug load consumption for the 8 zones in the South Triangle (kWh)</li> <li>Sanitary consumption for the 8 zones in the South Triangle (kWh)</li> <li>Blinds consumption for the 8 zones in the South Triangle (kWh)</li> <li>Air handling unit consumption for the South Triangle (kWh)</li> <li>Total electrical consumption for the South Triangle (kWh)</li> <li>Thermal energy consumption for VRF (Variable refrigerant flow) units of the South Triangle (kWh)</li> </ul> <p>Weather data include:</p> <ul> <li>Daily degree days (°C)</li> <li>Hourly humidity (%)</li> <li>Daily rain precipitation (mm)</li> <li>Hourly direct and global radiation (J/cm2)</li> <li>Daily sunshine hours (hrs)</li> <li>Hourly temperature (°C)</li> </ul> <p>The data set contains<br> - Timeseries as CSV files<br> - Timeseries metadata (description, unit, type,...) as JSON file</p> <p>The data have been made available from the BMS database, thanks to the BEMServer open-source platform - <a href="http://www.bemserver.org">www.bemserver.org</a>, developed in the HIT2GAP project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement n. 680708 - <a href="http://www.hit2gap.eu">www.hit2gap.eu</a>.</p>
Energy Consumption Synthetic Dataset Generation Through Parametric Analysis for Residential Buildings in Hot Climates
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On the energy consumption of test generation: Replication package
<p>Replication package for the paper "On the energy consumption of test generation"</p>
EAPBSdata: Energy consumption and bee dataset of five connected beehives of one high season in France
<p><strong>1- Introduction</strong></p> <p>This dataset was developed in the context of our work [1] that focuses on the orchestration of tasks between edge devices and cloud servers, and the energy optimization of orchestration scenarios applied to smart beehives. [1] introduces an energy-aware precision beekeeping system capable of collecting bee-related data and its own energy consumption. This system was deployed during the 2022 beekeeping high season in 5 beehives, 3 of which located in Lyon, France, and the 2 others in Cachan, France.</p> <p><strong>2- Description</strong></p> <p><strong>Hardware</strong></p> <p>The energy-aware precision beekeeping system relies on the Raspberry Pis:</p> <ul> <li>One Raspberry Pi 3b+ that periodically wakes up, collects bee-related data, transfers them and shuts down.</li> <li>One Raspberry Pi Zero WH that constantly collects the energy consumption of itself, the Raspberry Pi 3b+ and the photovoltaic intake.</li> </ul> <p>The energy node of our system relies on a solar panel, current converter, and battery. The solar panel is a monocrystalline 30 W panel. The battery is a 20 000 mAh power bank. Between the two, the current is converted to 5 volts (the adequate voltage for the battery) with a DC/DC Step-Down 5 volts/3 amperes converter.</p> <p>The current sensors connected to the Raspberry Pi Zero are Grove ±5A DC/AC current sensors based on the Allegro ACS70331 current sensor, which is based on giant magneto-resistivity sensing technology. This sensor is selected because of its adapted sensing range (0 to 5 A) and its wide temperature operating range (-40°C to 85°C), as the later-introduced setup operates in a sealed case in an outdoor environment. The current collection script is written in Python 3 and relies on Grove’s package functions.</p> <p><strong>Data collection routines</strong></p> <p>The Raspberry Pi Zero, which records the current, is always switched on. Every twelve minutes (except for beehive 5, where this frequency varies), it sends a signal through GPIO to wake up the other Raspberry Pi, the beehive data recorder. Once the Raspberry Pi 3b+ wakes up, it collects data from all its sensors, including three 10-second audio samples collected at the same time, five 800×600 pixels images spread over five seconds and temperature and humidity measurements of the inside of a beehive, and it transfers the data through Wi-Fi to a remote data storage cloud server. In addition, at regular intervals, the Raspberry Pi Zero WH transfers the latest consumption data.</p> <p>For now, we share the current data and the temperature/humidity. Later, we will share the audio and the image datasets.</p> <p>The date of the beehive data files from beehives 1, 2 and 3 do not match the current data due to the lack of network during the acquisition process. It prevented the Raspberry Pi 3b+ to update its internal clock during wake-up.</p> <p><strong>File Structure:</strong></p> <p>Beehives 1, 2 and 3 are located on the roof of a university (ENS de Lyon) in Lyon, whereas beehives 4 and 5 are located on the roof a university (aivancity Paris-Cachan) in Cachan. Beehives 1 and 4 met technical difficulties which lowered the amount of collected data, whereas beehives 2, 3 and 5 gathered data from April to July 2022</p> <table> <tbody> <tr> <td> <p><strong>Folder</strong></p> </td> <td> <p><strong>Number of files</strong></p> </td> </tr> <tr> <td> <p>beehive1/consumption</p> </td> <td> <p>6</p> </td> </tr> <tr> <td> <p>beehive1/temphumi</p> </td> <td> <p>37</p> </td> </tr> <tr> <td> <p>beehive2/consumption</p> </td> <td> <p>4158</p> </td> </tr> <tr> <td> <p>beehive2/temphumi</p> </td> <td> <p>2598</p> </td> </tr> <tr> <td> <p>beehive3/consumption</p> </td> <td> <p>3503</p> </td> </tr> <tr> <td> <p>beehive3/temphumi</p> </td> <td> <p>3120</p> </td> </tr> <tr> <td> <p>beehive4/consumption</p> </td> <td> <p>182</p> </td> </tr> <tr> <td> <p>beehive4/temphumi</p> </td> <td> <p>261</p> </td> </tr> <tr> <td> <p>beehive5/consumption</p> </td> <td> <p>1915</p> </td> </tr> <tr> <td> <p>beehive5/temphumi</p> </td> <td> <p>1736</p> </td> </tr> </tbody> </table> <p><strong>3- Citation</strong></p> <p>Works citing this dataset should also refer to [1] as the article that introduces the system used to collect the data.</p> <p><strong>4- Acknowledgment </strong></p> <p>The research that led to this dataset was made possible thanks to the funding from aivancity School for Technology, Business & Society Paris Cachan, and the contribution of École Normale Supérieure for the hardware.</p> <p> </p> <p>[1] H. Hadjur, D. Ammar, and L. Lefèvre, “Services Orchestration at the Edge and in the Cloud for Energy-Aware Precision Beekeeping Systems” 5th Workshop on Parallel AI and Systems for the Edge (PAISE 2023), an IPDPS workshop, 2023.</p> <p> </p>
Effect of Vestibular Stimulation on Fat Consumption and Energy Expenditure as Assessed Using Indirect Calorimetry
ClinicalTrials.gov study NCT03138382. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Experimental study on seismic performance of a low-energy consumption composite wall structure of a pre-fabricated lightweight steel frame
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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.