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47 results for “Energy Demand”
Case study input data set for article "Stochastic planning of energy system transformation pathways under uncertain industry demands"
<p>The data set contains input data for the model EMPRISE of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. </p>
SET-NAV: WP5: Invert modelling output for the building sector final energy demand and cost data
<p>This data set contains the Invert modelling results for final energy demand for space heating, cooling and hot water in buildings; hourly data for district heating and electricity (for different technologies) for 3-4 building types; annual data for the other energy carriers.</p> <p>It also contains all annual cost (Annuity of investments, O&M, fuel cost ) of electricity generation and / or heat generation and considered efficiency measures.</p> <p>This data is also available and visualised in our dedicated SET-NAV open data platform: The SET-NAV Scenario Explorer: https://data.ene.iiasa.ac.at/set-nav/#/workspaces</p>
Global gridded scenarios of residential cooling energy demand to 2050
<p># ggACene (global gridded Air Conditioning energy) projections</p> <p>### Output AC and AC electricity gridded data</p> <p>This repository hosts output data for SSPs126, 245, 370 and 585 on the estimated and future projected ownership of residential air conditioning (% of households), the related energy consumption (TWh/yr.), and the underlying population counts (useful to quantify the per-capita average consumption or the headcount of people affected by the cooling gap). These data are contained in the multi-layer .nc (NCDF) files, which can be opened and processed in any GIS software/library, or visualised in softwares such as Panoply.</p> <p>### Input data and analysis replication</p> <p>The repository also hosts input data to replicate the entire data generating process. A twin Github repository hosts code (<a href="https://github.com/giacfalk/ggACene">https://github.com/giacfalk/ggACene</a>) to run the model generating the ggACene (global gridded Air Conditioning energy) projections dataset.</p> <p>## Instructions<br>To reproduce the model and generate the dataset from scratch, please refer to the following steps:<br>- Download input data "replication_package_input_data.7z" by cloning the repository<br>- Decompress the folder using 7-Zip (https://www.7-zip.org/download.html)<br>- Open RStudio and adjust the path folder in the sourcer.R script<br>- Run the sourcer.R script to train the ML model, make projections, and represent result files<br><br></p> <p>### Figures replication package</p> <p>Finally, the source_code_data_replication_figures.zip archive contains an R script and processed input data to replicate all the figures contained in the manuscript.<br><br></p> <p>### Reference<br><br>Falchetta, G., De Cian, E., Pavanello, F., & Wing, I. S. Inequalities in global residential cooling energy use to 2050. Nature Communications. https://www.nature.com/articles/s41467-024-52028-8</p>
Data from: Built structures influence patterns of energy demand and CO2 emissions across countries
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Data to calculate metal demand from IAM energy projections and add flexibility to the technological mix
<p>In the "Data" folder, you’ll find all necessary data to run the <strong>"IAM_metal_optimisation.py"</strong> script, available on GitHub (<a href="https://github.com/Pelotte/iam-metal-demand-optimizer/tree/master" target="_new" rel="noopener">https://github.com/Pelotte/iam-metal-demand-optimizer/tree/master</a>).</p> <p>The <strong>"IAM_metal_optimisation.py"</strong> script is a tool designed to:</p> <ol> <li>Quantify metal supply and demand by sector through 2050, using energy projections from various IAMs and SSP-RCP scenarios.</li> <li>Optimize the IAM technological mix, minimizing adjustments needed to prevent metal demand from exceeding supply constraints.</li> </ol> <p>This tool supports all IAMs with power projections for SSP-RCP scenarios (except SSP3) provided in the IPCC’s Sixth Assessment Report (AR6), Working Group III, and available in the IIASA database.</p> <p>Data for RCP 2.6, SSPs 1, 2, 4, 5, of the SSP marker IAM models of the IPCC, are organized in the "Capacity Factor IAM," "GDP IAM," and "Power Capacity IAM" folders. See the GitHub README for more details.</p>
Energy demand and its temporal flexibility: approaches, criticalities and ways forward
<p>Data set of the reviewed documents in the contribution 'Energy demand and its temporal flexibility: approaches, criticalities and ways forward' under consideration for publication in 'Renewable & Sustainable Energy Reviews'</p>
Data Collection and Manipulation Template for MAED (Model for Analysis of Energy Demand)
<p>A Data Collection and Manipulation Template for MAED (Model for Analysis of Energy Demand). </p>
Norwegian hourly residential electricity demand data with consumer characteristics during the European energy crisis
<p>This dataset was collected to understand how Norwegian households responded to the electricity price shock due to the European energy crisis. It consists of consumer characteristics and their self-reported responses to the extraordinarily high electricity prices which were collected by a survey of 4,446 consumers. The consumer characteristics contain information about socio-demographics such as income, age, education, number of residents, residence type, residence size, and how conscious the respondents are about their electricity consumption. Furthermore, major electricity-consuming appliances are identified, such as whether the residents have an electric vehicle and how they heat their homes, and if they have a variable electricity tariff. In addition, hourly metered electricity consumption data covering October 2020 to March 2022 from a subset of 1,136 residential consumers of the surveyed households and the total hourly residential electricity consumption per Norwegian bidding area from July 2019 to July 2022as well as the hourly day-ahead electricity prices are included in the dataset. These data are interesting to researchers that aim to gain insight into the electricity consumption behaviour of the residential sector and the impact of different socio-demographic variables.</p> <p>A detailed description is available as a data article in Data in Brief: <a href="https://www.sciencedirect.com/science/article/pii/S2352340923007667">Norwegian hourly residential electricity demand data with consumer characteristics during the European energy crisis - 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>
Dataset for the publication: Flexible copper: exploring capacity-based energy demand flexibility in the industry
<p>This file contains the inputs for the study (currently under review) "Flexible copper: exploring capacity-based energy demand flexibility in the industry"</p>
Data from: Eat more, often: The capacity of piscivores to meet increased energy demands in warming oceans
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Influence of Nuclear Investment Costs and Baseload Demand on the Optimal Energy Mix
<p>Results from the study on "Influence of Nuclear Investment Costs and Baseload Demand on the Optimal Energy Mix".</p>
Data from: Computer simulations show that Neanderthal facial morphology represents adaptation to cold and high energy demands, but not heavy biting
Three adaptive hypotheses have been forwarded to explain the distinctive Neanderthal face: 1) an improved ability to accommodate high anterior bite forces, 2) more effective conditioning of cold and/or dry air, and, 3) adaptation to facilitate greater ventilatory demands. We test these hypotheses using three-dimensional models of Neanderthals, modern humans, and a close outgroup (H. heidelbergensis), applying finite element analysis (FEA) and computational fluid dynamics (CFD). This is the most comprehensive application of either approach applied to date and the first to include both. FEA reveals few differences between H. heidelbergensis, modern humans and Neanderthals in their capacities to sustain high anterior tooth loadings. CFD shows that the nasal cavities of Neanderthals and especially modern humans condition air more efficiently than does that of H. heidelbergensis, suggesting that both evolved to better withstand cold and/or dry climates than less derived Homo. We further find that Neanderthals could move considerably more air through the nasal pathway than could H. heidelbergensis or modern humans, consistent with the propositions that, relative to our outgroup Homo, Neanderthal facial morphology evolved to reflect improved capacities to better condition cold, dry air, and, to move greater air volumes in response to higher energetic requirements.
The Effects of Differing Cognitive Task Demands on Whole-body Energy Metabolism and Cerebral Blood-flow: Modulation by Multivitamins/Minerals and Coenzyme Q10
ClinicalTrials.gov study NCT02381964. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effects of Supradyn® Energy 3RDA on Fatigue/Stress, Substrate Metabolism During Exercise and Demanding Cognitive Tasks
ClinicalTrials.gov study NCT03003442. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Computer simulations show that Neanderthal facial morphology represents adaptation to cold and high energy demands, but not heavy biting
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Data from: Energy demand and the context-dependent effects of genetic interactions underlying metabolism
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Thermal and electrical energy demands from energy meters and Measured values from sensors in an air handling unit and in the served lecture room of an university building
<p>The dataset consists of measured values from 2 different energy meters installed in the Alice Perry Building of the NUIG university in Galway, Ireland, which measure respectively: (1) the thermal energy for heating delivered to the distribution loop towards the heating radiators of the thermal zone of the west part of the building, where many offices, and some meeting rooms and lectures rooms are located (measuring time interval of 1 minute, from April 2018 to the end of February 2019); (2) electrical energy demand for the chillers serving the whole building (daily values, from January 2018 to the end of February 2019). </p> <p>The dataset also contains measured values of physical variables, states and operating conditions from a detailed layout of data-points installed in an air handling unit, in the outdoor environment and in the related served room, which is a lecture theatre in the Alice Perry Building of the NUIG university in Galway, Ireland. The data have been made available for the period from January 2018 to the end of February 2019, at the measuring time interval of 1 minute. The variables list, with their explanations and graphical schemes, is available as attachment. </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>
Enhanced glucose metabolism through activation of HIF-1α covers the energy demand in a rat embryonic heart primordium after heartbeat initiation.
GEO Series GSE185702. Rattus norvegicus. 6 samples. Type: Expression profiling by array.
Climate-driven changes in electricity demand and energy expenditures
<p>These data are the full model outputs corresponding to the direct electricity demand & energy expenditure impacts reported in Hsiang et al. (2017), "Estimating economic damage from climate change in the United States," DOI 10.1126/science.aal4369. Model documentation can be found in that article and in Houser et al. (2015), "Economic Risks of Climate Change: An American Prospectus," ISBN 9780231174565.</p>
JRC - Raw materials demand for wind and solar PV technologies in the transition towards a decarbonised energy system - Materials demand database
<p>This dataset contains the results of the materials demand scenarios for wind and solar PV technologies developed in the following report by the European Commission's Joint Research Centre (JRC):</p> <p>Carrara S., Alves Dias P., Plazzotta B. and Pavel C., Raw materials demand for wind and solar PV technologies in the transition towards a decarbonised energy system, EUR 30095 EN, Publication Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-16225-4, doi:10.2760/160859, JRC119941</p> <p>The report can be found at the following link:</p> <p><a href="https://ec.europa.eu/jrc/en/publication/raw-materials-demand-wind-and-solar-pv-technologies-transition-towards-decarbonised-energy-system">https://ec.europa.eu/jrc/en/publication/raw-materials-demand-wind-and-solar-pv-technologies-transition-towards-decarbonised-energy-system</a></p>
ScienceDex guides
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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.