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31 results for “energy resources”
SEEtheSkills resources database from the Interregional research on the status of energy skills
<p>This dataset includes a list of resources identified during the interregional research on the status of energy skills, done in the frame of SEEtheSkills project. The comprehensive overview of the information created in the area of Energy Efficiency (EE) and Renewable Energy Systems (RES), goes both wide, by trying to identify as many different examples as possible, and deep, by digging into the examples themselves. The key areas the research focused on: skills defined in national roadmaps; skills developed as part of previous BUS projects; developed training schemes; the number of trained workers and professionals; companies that design and produce EE materials; status of Recognition of Previous Learning (RPL); status of demand for energy skills; level of awareness of energy skills; available certification schemes; legal obligations promoting the use of energy skills and their timelines, predictions for future development of energy skills. The survey covers mainly the five countries participating in the project Slovenia, Spain, Netherlands, Slovakia and North Macedonia, but also beyond their geographical coverage.</p>
Data from: Solar energy resource availability under extreme and historical wildfire smoke conditions
<p>The data in this repository are used to generate the figures in the article "Solar energy resource availability under extreme and historical wildfire smoke conditions" by Corwin et al. (accepted 2024) in <em>Nature Communications</em>. Data are the final processessed and merged datasets sourced from the following publicly available data products:</p> <ul> <li>National Renewable Energy Laboratory’s (NREL) National Solar Radiation Database (NSRDB) (<a href="https://nsrdb.nrel.gov/)">https://nsrdb.nrel.gov/)</a>. <ul> <li>Bulk download in July 2023 via AWS: <a href="https://registry.opendata.aws/nrel-pds-nsrdb/">https://registry.opendata.aws/nrel-pds-nsrdb/</a></li> <li>Variables: modeled irradiance (clear-sky and all-sky direct normal (DNI) and global horizontal (GHI) irradiance, aerosol optical depth, and cloud optical depth</li> </ul> </li> <li>National Oceanic and Atmospheric Administration’s (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) Hazard Mapping System (HMS) smoke product. <ul> <li>Access: <a href="https://www.ospo.noaa.gov/Products/land/hms.html#maps">https://www.ospo.noaa.gov/Products/land/hms.html#maps</a></li> <li>Variables: smoke plume locations</li> </ul> </li> <li>National Aeronautics and Space Administration's (NASA) Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product (MCD19A2 MODIS/Terra + Aqua land aerosol optical depth daily L2G Global 1km SIN Grid V006). <ul> <li>Access: <a href="https://lpdaac.usgs.gov/products/mcd19a2v006/">https://lpdaac.usgs.gov/products/mcd19a2v006/</a></li> <li>Variables: aerosol optical depth and cloud mask</li> </ul> </li> <li>NASA's Clouds and the Earth’s Radiant Energy System (CERES) cloud data product (SYN1deg-1Hour Edition 4.1) <ul> <li>Access: <a href="https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1degEd41Selection.jsp">https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1degEd41Selection.jsp</a></li> <li>Variables: cloud optical depth</li> </ul> </li> </ul> <p>A detailed description of the data processing methods used to produce the final merged data are available in the article by Corwin et al. </p> <p>Associated code scripts are located in the linked code repository.</p>
Bayesian Online Learning for Energy-Aware Resource Orchestration in Virtualized RANs - Dataset
<p>Dataset providing a set of measurement of performance and power consumpetion of a virtualized Base Station (srseNB).</p>
SHEERM: Sustainable Household Energy and Environment Resources Management dataset
<p>This dataset represents a novel and extensive dataset featuring comprehensive cross-sectional data of household electrical load, energy cost, and on-premises solar energy production, directly linked to solar radiation and weather parameters. <br>The SHEERM dataset is essential for understanding and optimizing energy utilization to achieve Sustainable Development Goals (SGD) 7, 9, 11 and 13. It provides data about solar energy production, weather conditions, residential energy needs, and market prices. The combination of these variables facilitates multifaceted analysis, fostering advancements in renewable energy forecasting, climate-sensitive environments, grid management, and energy policy formulation.<br>Together with the SHEERM dataset, there is a paper that details the data collection process, including the sources and methodologies employed. Adhering to established literature, we developed and implemented machine learning models that comprehensively validate the data. Furthermore, as usage notes, we offer additional results by applying various machine-learning approaches to the provided data.<br>The SHEERM dataset aims to help design new energy systems that enhance sustainable energy strategies and demonstrate their potential to accelerate the transition towards renewable energy and carbon neutrality.</p>
In-stream tidal energy resources in macrotidal non-cohesive sediment environments: effect of morphodynamic changes at two bays in the upper Gulf of California
<p>Project_info: This dataset was obtained during the project CeMIE-Oceano (2017-2021), and was party financed by SENER-CONACyT (contract no. 249795).<br> License: The authors appreciate that users of these data: 1) Contact Vanesa Magar (vmagar@cicese.edu.mx) to follow the uses of the data, and 2) Include the requested acknowledgment (cite using the DOI of this dataset) in any presentations or publications.</p> <p>This dataset includes data used for producing Figures 4,5 and Tables 1,2 of paper "IN-STREAM TIDAL ENERGY RESOURCES IN MACROTIDAL NON-COHESIVE SEDIMENT ENVIRONMENTS: EFFECT OF MORPHODYNAMIC CHANGES AT TWO BAYS IN THE UPPER GULF OF CALIFORNIA" published in<br> Journal of Marine Science and Engineering.</p> <p>Bermúdez-Romero, Anahí; Vanesa Magar; Markus S. Gross; Victor M. Godínez; Manuel López-Mariscal; Julio Candela. In-Stream tidal energy resources in macrotidal non-cohesive sediment environment: Effect ofmorphodynamic changes at two bays in the upper Gulf of California. Journal of Marine Science and Enginnering, 9:411. https://doi.org/10.3390/jmse9040411</p>
U.S. building energy efficiency and flexibility as an electric grid resource (Data and Code)
<p><strong>* New in Version 2.1 *</strong></p> <ul> <li> <p>All residential measure savings shapes data (<strong>Latest_Res_Shapes.zip</strong> and residential measures in <strong>Latest_BM_Shapes.zip</strong>) were updated to correct post-processing errors present in version 2.</p> </li> <li> <p>The raw baseline-case data that are used in Scout to estimate sector-level baseline hourly loads (file <a href="https://github.com/trynthink/scout/blob/master/supporting_data/tsv_data/tsv_load.gz">tsv_load</a>) are now included in this data resource (see files <strong>Latest_Res_Baselines.zip</strong> and <strong>Latest_Com_Baselines.zip</strong>).</p> </li> <li> <p>Additional residential measure run documentation is available (<a href="https://github.com/NREL/resstock/blob/e2a98b7345d5c453ba35341b70af2f8859dd22fe/GEB_Potential.yml">here</a> for all except water heating efficiency plus flexibility (EE+DF) measure and <a href="https://github.com/NREL/resstock/blob/9611d92388e1e23466c9dc451e115c21321b4012/GEB_Potential_v2.5.0_appl_ee_dr.yml">here</a> for the water heating EE+DF measure).</p> </li> <li>A guide to reading and/or preparing savings shapes CSVs is available <a href="https://scout-bto.readthedocs.io/_/downloads/en/latest/pdf/">in the Scout documentation</a>, p. 36. The documentation also summarizes the net system load conditions that measures with flexibility (DF) characteristics respond to (Table 1, p. 37).</li> </ul> <p><strong>* New in Version 2 *</strong></p> <p>All hourly savings shapes CSV files that support the original <a href="https://doi.org/10.1016/j.joule.2021.06.002">analysis</a> have been updated to reflect the following improvements:</p> <ul> <li> <p>Generate residential data using ResStock v2.5.0 and commercial data using DOE Commercial Prototypes generated with OpenStudio v3.3.0.</p> </li> <li> <p>Residential and commercial measures with flexibility (DF) features respond to updated grid conditions (net peak/low load periods) that are consistent with projections from the EIA 2022 Annual Energy Outlook (AEO) “Low renewables cost” <a href="https://www.eia.gov/outlooks/aeo/tables_side_xls.php">side case</a>.</p> </li> <li> <p>Residential baseline loads and load savings are now distinguished by three building types (single family, multi family, and mobile homes).</p> </li> </ul> <p>Updated savings shape CSVs are organized into three ZIP files that may be separately downloaded depending on user interests:</p> <p><strong>Latest_BM_Shapes.zip</strong> includes only the subset of savings shape CSVs needed to execute the <a href="https://doi.org/10.5281/zenodo.3158929">Scout Benchmark Scenarios</a>.</p> <p><strong>Latest_Res_Shapes.zip</strong> includes all residential savings shape CSVs.</p> <p><strong>Latest_Com_Shapes.zip</strong> includes all commercial savings shape CSVs.</p> <p>Baseline load shapes in Scout have also been updated based on the same versions of ResStock and the DOE Commercial Prototypes, and peak/take period impact calculations have been updated to reflect the 2022 AEO system conditions. These updated data are contained in <a href="https://github.com/trynthink/scout/releases/tag/v0.8">Scout v0.8</a> (see ./supporting_data/tsv_data).</p> <p><br> <strong>Summary of Original Data Files</strong></p> <p>These data underpin an analysis of the near- and long-term technical potential bulk power grid resource offered by best available U.S. building efficiency and flexibility measures. Using multiple openly-available modeling frameworks supported by the U.S. Department of Energy, including <a href="https://scout.energy.gov/">Scout</a>, <a href="https://resstock.nrel.gov/">ResStock</a>, and the <a href="https://www.energycodes.gov/development/commercial/prototype_models">Commercial Building Prototype Models</a>, we pair bottom-up simulations of measures' building-level impacts with regional representations of the building stock and its projected electricity use to estimate the impacts of multiple building efficiency and flexibility scenarios on hourly regional system loads across the contiguous U.S. in 2030 and 2050. We find that demand-side management via building efficiency and flexibility could avoid up to nearly ⅓ of annual fossil-fired generation and ½ of fossil-fired capacity additions after 2020.<strong> </strong>Results are reported at both the national and regional scales and are disaggregated by building type and end use, facilitating a quantitative understanding of the role that buildings as a whole and specific building technologies or operational approaches can play in the future evolution of the U.S. electricity system.</p> <p>The four ZIP files that make up this data record are interpreted as follows:</p> <p><strong>Measure_Data.zip: </strong>Includes the Scout energy conservation measure (ECM) JSON definitions that were used to generate the main baseline and efficient/flexible scenario results ("Baseline_Measures" and "Efficiency_Flexibility_Measures", respectively), as well as side cases that assess the sensitivity of results to higher levels of variable renewable penetration ("High_RE_Sensitivity_Analysis") and a high degree of building load electrification ("High_Electrification_Measures"). Each measure set includes supporting 8760 load savings shapes in the sub-folder "Savings_Shapes". Additional details about defining and interpreting Scout measures with time-sensitive analysis features are available <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#time-sensitive-valuation">here</a>.</p> <p><strong>Results_Data.zip: </strong>Includes the main and side case results data. Baseline-case outcomes, which are consistent with the <a href="https://www.eia.gov/outlooks/archive/aeo19/">EIA 2019 Annual Energy Outlook</a>, are stored in "Baseline_Loads". Efficient/flexible scenario results are stored in "Efficiency_Flexibility_Measure_Impacts_Individual" and "Efficiency_Flexibility_Measure_Impacts_Portfolio," respectively, where the former includes results for individual measures in our analysis without considering any interactions across measures, and the latter includes results for aggregations of energy efficiency (EE), demand flexibility (DF), and efficiency and flexibility (EE+DF) portfolios that do consider interactions across measures in each portfolio. Results for the high electrification side case are stored in the "High_Electrification" sub-folder in the EE+DF case only. Results for the high renewable sensitivity analysis are stored in "High_RE_Sensitivity_Analysis", and residential and commercial 8760 savings shape outcomes for each of the EE, DF, and EE+DF measure portfolios and five of the 2019 EIA Electricity Market Module (EMM) <a href="https://www.eia.gov/outlooks/aeo/nems/documentation/archive/pdf/m068(2018).pdf">regions</a> (p.6) of focus are stored in "Sector_Level_8760s".</p> <p><strong>Source_Code.zip: </strong>Includes the source code needed to translate the measure inputs provided in "Measures_Data.zip" into the outputs provided in "Results_Data.zip". The core set of files required to execute the main analysis results is stored in "Base_Code_Package", while variants to certain files in the core package needed to execute the high renewable sensitivity and high electrification side cases are stored in "Code_Variants". In general, the process of running an analysis is as described in the Scout <a href="https://scout-bto.readthedocs.io/en/latest/quick_start_guide.html">Quick Start Guide</a>; however, the file "ecm_prep_batch.py" should be substituted for "ecm_prep.py" and the file "run_batch.py" should be substituted for "run.py". These batch files execute multiple versions of "ecm_prep.py" and "run.py" that are tailored to generate individual measure and whole portfolio results for annual, net peak summer and winter, and net off-peak summer and winter metrics (individual measures: "ecm_prep.json," "ecm_prep_spa," "ecm_prep_wpa," "ecm_prep_sta," "ecm_prep_wta"; whole portfolio: "ecm_results.json," "ecm_results_spa.json," "ecm_results_wpa.json," and "ecm_results_sta.json," and "ecm_results_wta.json"). Results for the side cases are generated by replacing the versions of the "ecm_prep" and "run" files included in the "Base_Code_Package" folder with those in the "Code_Variants" folder. Sector-level 8760 shapes are generated using the "--sect_shapes" command line option as described <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#sector-level-hourly-energy-loads">here</a>. See Scout's <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#local-execution-tutorials">Local Execution Tutorials</a> for more details on how to develop Scout inputs and outputs.</p> <p><strong>Supporting_Data.zip: </strong>Includes supplemental data files provided by EIA that describe key inputs and outputs to the <a href="https://www.eia.gov/outlooks/aeo/nems/documentation/archive/pdf/m068(2018).pdf">Electricity Market Module</a> in the AEO 2019 run of the National Energy Modeling System ("EIA EMM Data (AEO 2019)"), as well as raw EnergyPlus outputs that were used to develop the baseline Scout hourly load shape file found in "./Source_Code/Base_Code_Package/supporting_data/tsv_data/tsv_load.json". </p>
Energy allocation explains how protozoan phenotypic traits change in response to temperature and resource supply
Open the record for dataset details and reuse information.
Supporting data for Open government data for regulation of energy resources in India
<p>This dataset contains supporting tables from the paper "Open government data for regulation of energy resources in India" published as part of the Exploring the Emerging Impacts of Open Data in Developing Countries project.</p> <p>Contextual information on each table is provided in the associated report.</p>
Data from: Comparing the importance of farming resource endowments and agricultural livelihood diversification for agricultural sustainability from the perspective of the food–energy–water nexus
<p>Household Survey Data for journal article 'Comparing the importance of farming resource endowments and agricultural livelihood diversification for agricultural sustainability from the perspective of the food–energy–water nexus', without protected data.</p>
Resources for "BMF CP 99: Parents as motivations for children's energy conservation behaviors"
<p>The current study is conducted to examine the following research questions:</p> <ul> <li>How are the father’s interactions with children regarding energy saving associated with the children’s energy conservation behaviors?</li> <li>How are the mother’s interactions with children regarding energy saving associated with the children’s energy conservation behaviors?</li> </ul>
HADES: An NFV solution for energy-efficient placement and resource allocation in heterogeneous infrastructures. Study dataset
<p>The publication and research associated with this dataset are currently under review in the Journal of Network and Computer Applications.</p> <p>In that research, we present HADES, an NFV solution for energy-efficient placement and resource allocation in heterogeneous infrastructures. HADES is an OSM (Open Source MANO) extension that allows the configuration of virtual network functions and their subsequent resource allocation and deployment at the edge, minimizing energy consumption and ensuring the quality of service.</p> <p>This dataset contains the following:</p> <ul> <li>1 CSV file with execution time results of the iTAREA module for (60) different problem sizes and (3) execution environments.</li> <li>1 CSV file with energy consumption results of HADES deployments and other 5 assignment policies: First-fit, Random-fit, Fastest-fit, Best-fit, and kube-scheduler (the Kubernetes’ assignment policy).</li> </ul> <p>This work is supported by the European Union's H2020 research and innovation programme under grant agreement DAEMON 101017109 and by the projects co-financed by FEDER funds LEIA UMA18-FEDERJA-15 and IRIS PID2021-122812OB-I00 (MCI/AEI).</p>
Data set for Dynamic Service Restoration of Distribution Networks with Volt-Var Devices, Distributed Energy Resources, and Energy Storage Systems
<p>Two power distribution systems are presented. The first system consists of 53 nodes and 61 branches, while the second system consists of 404 buses and 430 branches. Both distribution systems offer extensive applications in problems related to multi-time service restoration, Volt/Var devices, and distributed energy resource operation.</p>
US federal resource allocations are inconsistent with concentrations of energy poverty
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Resource selection and survival of plains sharp-tailed grouse at a wind energy facility
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Resource gradients create energy trade-offs in the inducible defense response of <em>Paramecium aurelia</em>
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Data from: Hummingbirds budget energy flexibly in response to changing resources
1. Managing energy stores in response to variable resource availability is a key component to individual fitness, yet because directly measuring energy budgets is difficult, daily energy management is rarely measured. 2. Hummingbirds' energy management is relatively simple to model compared to other endotherms because they have high mass-specific metabolic rates and store little fat. 3. We determined which aspects of the hummingbird daily energy budget (i.e. thermoregulation, daytime activity costs, nighttime costs) change at the individual level in response to environmental variation. 4. We found that daily energy expenditure varied three-fold in two populations of broad-billed hummingbirds (Cynanthus latirostris). 5. Our model indicated the energy budget was distributed in the following proportions: daytime activity, 59% (range 22-84%); thermoregulation, 23% (11 – 32%), basal metabolism, 7% (3 – 16%), and nighttime energy 17% (6 – 37%). Activity costs were higher at the hotter, homogeneous site and during the early-wet season at both sites. 6. Increased daily energy expenditure was related to decreased nectar availability, and not significantly related to temperature or bird mass. With climate change, the indirect energetic costs of shifting resources could have greater impacts on endotherm energy budgets than direct costs such as thermoregulation. Increased foraging and activity costs could decrease the energy available to birds for somatic repair and reproduction, potentially causing differential fitness across seasons and sites.
Skillful Seasonal Prediction of Wind Energy Resources in the contiguous United States
<p>This dataset contains data files used to replicate figures in a paper published in communications earth & environment. These datasets are in .mat format, which can be readable by Matlab software.</p>
Dataset for Energy Resource Management Considering Participation in the Wholesale Day-Ahead Market
<p>This release is associated with a paper entitled "A Novel Framework for Day-Ahead Market Clearing Process Featuring a Hybrid Pricing Mechanism".</p> <p>In this document, we provide the mathematical manipulation employed to linearize an MPEC problem that models the market-clearing process considering the participation of generation companies and distribution operators in the day-ahead market. Additionally, we provide a numerical example of the pricing mechanism designed for solving the optimization problem. </p> <p>Finally, we include information regarding the transmission and distribution power systems adopted to present the results shown in the paper. These systems are modified versions of the IEEE <a href="https://icseg.iti.illinois.edu/ieee-14-bus-system/">14-bus</a> and the IEEE <a href="https://cmte.ieee.org/pes-testfeeders/resources/">34-bus</a>, respectively. Three renewable non-dispatchable generators were added to the original 14-bus system, while 5 dispatchable generators and 3 non-dispatchable generators were added to the distribution system. 13 load shapes were considered as well as 5 scenarios for each uncertain variable (wind velocity, solar irradiance, distribution load oscillation, and transmission load oscillation). The data regarding the costs and physical parameters of each of the power system's elements are provided in this document.</p>
Dataset for Energy Resource Management Considering Participation in the Wholesale Day-Ahead Market
<p>This release is associated with a paper entitled "A Novel Bilevel Programming Formulation for Optimizing the Interaction of Distribution System Operators and GENCOs in Day-Ahead Markets".</p> <p>In this document, we provide the mathematical manipulation employed to linearize an MPEC problem that models the market-clearing process considering the participation of generation companies and distribution operators in the day-ahead market. Additionally, we provide a numerical example of the pricing mechanism designed for solving the optimization problem. </p> <p>Finally, we include information regarding the transmission and distribution power systems adopted to present the results shown in the paper. These systems are modified versions of the IEEE <a href="https://icseg.iti.illinois.edu/ieee-14-bus-system/">14-bus</a> and the IEEE <a href="https://cmte.ieee.org/pes-testfeeders/resources/">34-bus</a>, respectively. Three renewable non-dispatchable generators were added to the original 14-bus system, while 5 dispatchable generators and 3 non-dispatchable generators were added to the distribution system. 13 load shapes were considered as well as 5 scenarios for each uncertain variable (wind velocity, solar irradiance, distribution load oscillation, and transmission load oscillation). The data regarding the costs and physical parameters of each of the power system's elements are provided in this document.</p>
Dataset for Energy Resource Management Considering Participation in the Wholesale Day-Ahead Market
<p>This release is associated with a paper entitled "A Novel Framework for the Day-Ahead Market Clearing Process Featuring the Participation of Distribution System Operators and a Hybrid Pricing Mechanism".</p> <p>In this document, we provide the mathematical manipulation employed to linearize an MPEC problem that models the market-clearing process considering the participation of generation companies and distribution operators in the day-ahead market. Additionally, we provide a numerical example of the pricing mechanism designed for solving the optimization problem. </p> <p>Finally, we include information regarding the transmission and distribution power systems adopted to present the results shown in the paper. These systems are modified versions of the IEEE <a href="https://icseg.iti.illinois.edu/ieee-14-bus-system/">14-bus</a> and the IEEE <a href="https://cmte.ieee.org/pes-testfeeders/resources/">34-bus</a>, respectively. Three renewable non-dispatchable generators were added to the original 14-bus system, while 5 dispatchable generators and 3 non-dispatchable generators were added to the distribution system. 13 load shapes were considered as well as 5 scenarios for each uncertain variable (wind velocity, solar irradiance, distribution load oscillation, and transmission load oscillation). The data regarding the costs and physical parameters of each of the power system's elements are provided in this document.</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.