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21 results for “Electricity Consumption”
Electricity consumption and real vaue-added data for the Swiss and Genevan secondary and tertiary sectors.
<p>This file contains datasets of electricity consumption and real value added (base year 2000) in the secondary and tertiary sector for Switzerland and Geneva for the years between 2000 and 2015. The structure of the Swiss and Genevan datasets has been matched to ensure comparability of results from index decomposition analyses conducted on each region. It also contains heating and cooling degrees for both Switzerland and Geneva.</p>
Electricity Consumption and Meteorology in Sceaux
<p>Householder electricity consumption data accompanied with meteorological data at Sceaux, France.</p>
A rich dataset of hourly residential electricity consumption data and survey answers from the iFlex dynamic pricing experiment
<p>An experiment was conducted to understand if and how households change their power consumption in response to variable hourly electricity prices. The data were collected from several Norwegian regions, and various price signals were tested over two winter periods from early 2020 to spring 2021. The dataset includes hourly consumption data of all participating households and answers to three surveys about household characteristics such as electric appliances, living conditions, socio-demographic variables, and willingness to be flexible. Temperature data are added to the dataset from public sources. This comprehensive dataset can be used for in-depth analysis of household flexibility potential. Furthermore, subgroups, such as low-income households or highly electrified households with electricity as a primary heating source, can be investigated to enhance the understanding of how these are affected by variable power prices.</p> <p>The dataset is described in detail in an accompanying data article in Data in Brief: <a href="https://www.sciencedirect.com/science/article/pii/S2352340923006716">A rich dataset of hourly residential electricity consumption data and survey answers from the iFlex dynamic pricing experiment - ScienceDirect</a></p> <p>Supplementary figures containing the survey results are available here: <a href="../records/11580541">Supplementary result diagrams from household surveys on implicit demand response (zenodo.org)</a></p> <p>Survey answers in Norwegian are available here: <a href="https://zenodo.org/records/15063303">iFleks-prosjekt: Spørreundersøkelser med husholdninger og næringsliv om forbruksrespons på elektrisitetspriser</a></p>
Electrical consumption of one building located at the Campus of University of Girona.
<p>This dataset is a supplement of the the following paper "Identifying services for short-term load forecasting using data driven models in a Smart City platform". Authors: J. Massana, C. Pous et al.Journal: Sustainable Cities and Society, Vol. 28, Jan. 2017, pp. 108-117. <a href="https://doi.org/10.1016/j.scs.2016.09.001">https://doi.org/10.1016/j.scs.2016.09.001</a></p> <p>Each excel file contains the electrical consumption of one building located at the Campus of University of Girona.</p> <p>Data were collected from 2011 to 2014.</p> <p>Column information for the excel files:</p> <ul> <li>Hora: hour of the day (0, 1... 23).</li> <li>Dia: day of the month (1, 2... 31).</li> <li>Dia_set: day of the week (1,2... 7).</li> <li>Mes: month (1,2... 12)</li> <li>Any: year (2011... 2014).</li> <li>C: electrical consumption (kWh)</li> </ul>
SPARCS_WP3_Espoo_City_Electricity consumption in Espoo, Finland
<p>Electricity consumption in Espoo, Finland, divided by sector. Provided by the Helsinki Region Environmental Authority. 2000-2023</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>
UK Electricity consumption time-series from Elexon data portal (Actual Total Load Per Bidding Zone)
<p>Data from 2015-01-01 to 2023-08-10. Downloaded using ElexonDataPortal for Python.</p> <p>Dataset B0610 – Actual Total Load per Bidding Zone: <a href="https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=0CDYQw7AJahcKEwjw1P2089yAAxUAAAAAHQAAAAAQAw&url=https%3A%2F%2Fwww.elexon.co.uk%2Fdocuments%2Fbmrs-api-and-data-push-guide-for-p408%2F&psig=AOvVaw3JTwF_pxNDLFZp3HSDJa_s&ust=1692128319855369&opi=89978449">https://www.elexon.co.uk/documents/bmrs-api-and-data-push-guide-for-p408/</a></p>
2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics
<p> </p> <p>This dataset is the second release of data for the <a href="https://www.gecad.isep.ipp.pt/ERM-competitions/2025-energy-forecast/">2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics</a></p> <p>The competition is open and welcomes everyone who wishes to participate and to anyone who can benefit from these data.</p> <h2>Competition Outline</h2> <p>Forecasting of electric energy consumption can be a very difficult tasks when handling building-level data. However, an accurate forecast is needed to boost the potential of energy management systems. The need to forecast energy consumption grows as our reliance on renewable energy sources, such as solar and wind power, grows. This means that to meet consumer demand with renewable energy generation, energy management systems must operate based on accurate energy forecasting models for both short and long-term periods. Energy consumption forecasting techniques that can manage a variety of scenarios, including varying prediction timeframes, accessible data, data frequency, and even data quality, have been the subject of intense research. There is no one-size-fits-all approach, where certain situations call for different approaches. The goal of this competition is to compile and evaluate the most recent advances in energy consumption forecasting techniques.</p> <p> </p> <h2>Releases Details</h2> <ul> <li><strong>v1.0</strong>: one year of data from a smart building with readings taken every 5 minutes.</li> <li><strong>v2.0</strong>: 40 days of data from a smart building with readings taken every 5 minutes.</li> <li><strong>v3.x</strong>: a single day of data from a smart building with readings taken every hour. These releases will become available during the first competition period (from 06/01/2025 to 10/01/2025).</li> <li><strong>v4.x</strong>: a single day of data from a smart building with readings taken every hour. These releases will become available during the second competition period (from 14/07/2025 to 18/07/2025).</li> </ul> <p> </p> <h2>Dataset Description</h2> <p>All releases are composed of the following data:</p> <ul> <li>Time: in hours and minutes</li> <li>Power: in Watts</li> <li>Voltage: in Volts</li> <li>Current: in Ampers</li> <li>Generation power: in Watts</li> <li>Temperature: in ºC</li> </ul>
Miscellaneous Electric Loads: Dataset of Unit Energy Consumption
<p>Spreadsheet dataset of unit energy consumption of 36 residential and commercial miscellaneous electric loads (MELs), with forecasts out to 2030. This is an update from version 1.0.0 (published September 2019) that contains two key changes:</p> <ol> <li>A calculation error on the annual energy consumption for residential security systems has been updated.</li> <li>Residential laptop computers are now explicitly analyzed and have a dedicated tab in the spreadsheet.</li> </ol> <p>Sources for all data are provided in the first tab (called "Legend and Sources").</p>
Profitability of alternative battery operation strategies in pho-tovoltaic self-consumption systems under current regulatory framework and electricity prices in Spain
<p>The decreasing costs of solar photovoltaic (PV) technology have led to an exponential growth in the use of PV self-consumption systems. This development has encouraged the consideration of battery energy storage systems (BESS) as a potential means of achieving even more independence from the fluctuating grid electricity prices. As PV technology and energy storage costs continue to decline, both technologies will likely play an increasingly important role in the renewable energy sector.</p> <p>The profitability of batteries in PV self-consumption systems is largely influenced by the price of consumed electricity and the price at which surplus energy is remunerated. However, strategies in PV-BESS self-consumption systems typically do not take electricity prices into consideration as a variable for decision-making. This study simulates and analyzes battery operation strategies that take into account electricity prices. The simulations are performed using real industrial consumption data and real electricity prices and tariffs, they cover the entire lifespan of the batteries, and include aging and degradation due to use and cycling. A techno-economic model is used to evaluate the advantages of incorporating these battery operational strategies into an actual PV-BESS system. The results demonstrate that the proposed strategies enhance the savings that batteries can provide.</p>
Metabolism and Oxygen Consumption During Functional Electrical Stimulation Cycling in COPD
ClinicalTrials.gov study NCT02594722. IPD Sharing: Not stated. Countries: 1. Publications: 2.
HOUSEFUL - Sensors data & electrical consumption of residential buildings
Open the record for dataset details and reuse information.
Dataset of Pixel-Level Electric Power Consumption (EPC) in the Belt and Road Region from 2000 to 2019
<p>Data name: Pixel-level dataset of electric power consumption (EPC) in the regions along the Belt and Road from 2000 to 2019<br> <br> Data format: GeoTIFF</p> <p>Spatial resolution: 1ⅹ1 km</p> <p>Data unit: Million kWh/km²<br> <br> Spatial reference: <br> Projection: World_Mollweide<br> Central_Meridian: 0<br> Geographic Coordinate System: GCS_WGS_1984<br> Datum: D_WGS_1984</p>
A Comparison Between the Aysis® Cs2 (General Electrics) Ventilator in EtControl® Mode and the Perseus® A 500 (Dräger) in VaporView® Mode on Maniability and Consumption of Desflurane
ClinicalTrials.gov study NCT02427971. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Transcutaneous Electrical Acupoint Stimulation for Opioid Consumption After Gastrointestinal Laparoscopic Surgery
ClinicalTrials.gov study NCT06024200. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Surface Electrical Myography, Oxygen Consumption, Effort, and Weaning in the Mechanically Ventilated Patient in the Intensive Care Unit
ClinicalTrials.gov study NCT07109570. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Influence of the Consumption of Conventional and Electric Cigarettes on Skin Circulation
ClinicalTrials.gov study NCT04645914. IPD Sharing: NO. Countries: 1. Publications: 0.
domOS Sion Living Lab: Household and Heat Pump Electricity Consumption Data
<p>This dataset has been collected in context of the domOS H2020 project (<a href="https://www.domos-project.eu/">https://www.domos-project.eu/</a>). The dataset includes data about the electricity consumption of 15 single-family residential households all located around Sion (Switzerland), in the OIKEN service area (<a href="https://oiken.ch">https://oiken.ch</a>). All the households are equipped with an individual heat pump providing energy for both space heating and domestic hot water.</p> <p>The ongoing data collection began in January 2021 and includes the households’ total electricity consumption provided by the Smart Meter. It also includes the specific consumption of the heat pump and the production of the solar panels (where applicable) provided by dedicated sub-meters.</p>
Effects of Acu-Transcutaneous Electrical Nerve Stimulation (Acu-TENS) on Post-exercise Blood Lactate and Excessive Post-exercise Oxygen Consumption (EPOC)
ClinicalTrials.gov study NCT01102634. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Typical daily generation and consumption electrical power profiles per month in three public buildings
<p>Electrical power values in kW representing the daily average profile of PV production and building consumption per month in three Mediterannean countries: Cyprus, Greece, and Italy. The time step of the data sets is 15min. The PV production is provided per kWp excluding the inverter losses, i.e., at the dc side of the inverter, based on data from PVGIS platfrom for the case of Cyprus and Italy, and on real measurements for the case of Greece.</p>
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