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72 results for “building energy”
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>
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 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>
Data Sources for Archetype-based Energy and Material Use Estimation for the Residential Buildings in Arab Gulf Countries
<p><strong>Dataset Name:</strong><br> <em>Literature Data and Archetype Parameter Sheets for the publication, named Archetype-based Energy and Material Use Estimation for the Residential Buildings in Arab Gulf Countries</em>.</p> <p><strong>Description:</strong><br> This dataset includes Excel sheets containing literature sources and archetypal data on GCC countries' residential dwelling typologies.</p> <p><strong>Files:</strong><br> The following files are included in the dataset:</p> <ul> <li> <em>[CountryName]_LiteratureSources.xlsx:</em> Excel sheet containing literature sources and references,</li> <li> <em>[CountryName]_ArchetypeParameters.xlsx</em>: Archetype models' semantic, geometric, and technical data used in the generation of energy models,</li> <li> <em>[CountryName]_Schedules.xlsx:</em> Excel sheet containing the operation schedules for countries. The sheet is compiled from literature sources and reorganized by expert consensus and given in Designbuilder input format,</li> <li> <em>[CountryName]_Stock.xlsx:</em> Excel sheet containing additional data on the building stock,</li> <li><em> VacantHouses.xlsx</em>: Vacant house rates for the countries, the found articles on the web, literature sources, etc.,</li> </ul> <p><strong>Usage:</strong><br> The dataset is intended for researching and analyzing the GCC countries' residential buildings. The literature sources included in the [CountryName]_LiteratureSources.xlsx and [CountryName]_ArchetypeParameters.xlsx files can be used to verify, support, or reproduce the research findings.</p> <p><strong>License:</strong><br> The dataset is licensed under Creative Commons Attribution 4.0 International.</p> <p><strong>Citation:</strong><br> If you use this dataset in your research, please cite it as follows and contact the corresponding author:</p> <p>Akin, Sahin, Chibuikem Chrysogonus Nwagwu, Niko Heeren, and Edgar Hertwich. 2023. “Archetype-Based Energy and Material Use Estimation for the Residential Buildings in Arab Gulf Countries.” Energy and Buildings 298: 113537. https://doi.org/https://doi.org/10.1016/j.enbuild.2023.113537.</p> <p><strong>Contact:</strong><br> The archetypes' energy models (DesignBuilder or IDF files) can be provided on request. If you have any questions or comments about the dataset, please contact <strong>sahin.akin@ntnu.no, the corresponding author.</strong></p>
Probono - Systematic Review - Energy-related behaviours of building occupants.
<p>This database covers the relevant literature on energy-related behaviours of building occupants, based on a systematic interrogation of existing research from 2018-2022. Data is collected and presented on the study location, research method, building type and type of occupants, occcupants behaviour, etc. This dababase allows for investigating where occupant behaviours are likely to be most prevalent and targetable for future research in Probono.</p>
Build-in-Wood Regulation Analysis – Energy and Indoor Environment
<p>This dataset contains an analysis of selected EU Member State building regulations covering energy and indoor environment in multi-storey wood buildings. The data has been collected as part of the Build-in-Wood project (<a href="https://www.build-in-wood.eu/)">https://www.build-in-wood.eu/)</a> which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 862820.</p> <p>Disclaimer: The presented data might be outdated, flawed, or otherwise incomplete. Users are responsible for checking the correctness of the presented data.</p>
Questionnaire on needs and requirements for energy performance gap reduction at the operational level of a building
<p>Questionnaires provided within the Hit2Gap H2020 project.</p> <p>Lsits needs and requirements of the different actors of the project; these actors are at different responsibilities of a building.</p>
Model America - Summer 2020 Arizona Building Energy Simulation Results from ORNL's AutoBEM
<p>Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (<a href="https://bit.ly/AutoBEM">bit.ly/AutoBEM</a>).</p> <p>Data is provided for 2,555,152 buildings located within the boundary of Arizona in the United States:</p> <p><strong>Data (1.48GB *.csv) - Arizona 2,555,152 building information data with simulation results separated by county. (Simulation results are for June 1st-August 31st, 2020)<br></strong></p> <p><strong>Building Information Data Fields:</strong></p> <ul> <li>ID</li> <li>CZ</li> <li>Centroid</li> <li>State_Abbr</li> <li>Footprint2D</li> <li>Height,Area2D</li> <li>BuildingType</li> <li>NumFloors</li> <li>Area</li> <li>Standard</li> <li>NumWalls</li> <li>WWR_surfaces</li> </ul> <p><strong>Energy Simulation Data Fields:</strong></p> <ul> <li>Electricity_Facility[kBTU]</li> <li>NaturalGas_Facility[kBTU]</li> <li>Heating_Electricity[kBTU]</li> <li>Cooling_Electricity[kBTU]</li> <li>Heating_NaturalGas[kBTU]</li> <li>Heating_Total[kBTU]</li> <li>WaterSystems_Electricity[kBTU]</li> <li>Lighting_Electricity[kBTU]</li> <li>Equipment_Electricity[kBTU]</li> <li>Fans_Electricity[kBTU]</li> <li>Pumps_Electricity[kBTU]</li> <li>HeatRejection_Electricity[kBTU]</li> <li>HeatRecovery_Electricity[kBTU]</li> <li>Surface_Outside_Face_Heat_Emission[GJ]</li> <li>Zone_Exfiltration_Heat_Loss[GJ]</li> <li>Zone_Exhaust_Air_Heat_Loss[GJ]</li> <li>Heat_Rejection_Energy[GJ]</li> <li>Anthropogenic_Emissions[GJ]</li> </ul> <p>This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), and Biological and Environmental Research (BER).</p>
Supplemental idf files used for paper "Refurbishmet Methodology to Attain Thermally Comfortable near-Zero Energy Buildings Using Customizable Solutions"
<p>These are the idf files used to study different technology alternatives offered by the Reco2st project on an idealized dormitory room located in London, UK. The files are presented "as is" and were done for EnergyPlus v8.9. Includes files that might have not made it to the final paper.</p> <p>Geometry: One zone room with one external window.<br> Materials: External construction is cavity wall, with bricks on both sides, gypsum plaster on internal side<br> Internal walls brick single layer with gypsum plaster.<br> Ceiling and floor reinforced concrete. Plaster finish on ceiling, nylon carpet on floor<br> Double glazed window. Five panes available: one large central fixed, two small clerestory opening, and two large side opening.<br> For simulation purposes a single equivalent area of all openings modelled.<br> Material data sources: gov.scot, puravent.co.uk, nature.com. SHGC from LBL.</p> <p>This is part of work done for ReCO2ST - Residential Retrofit assessment platform and demonstrations for near zero energy and CO2 emissions with optimum cost, health, comfort and environmental quality. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 768576.</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Kortrijk Kennedy Park, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Kortrijk Kennedy Park (50°48'2"N 3°16'13" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Antwerp Berchem, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Antwerp Berchem (51°12'00"N 4°26'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Sint-Katelijne-Waver, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Sint-Katelijne-Waver (51°3'25"N 4°11'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Uccle KMI, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Uccle KMI (50°47'49"N, 4°21'29" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Leuven City centre, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Leuven City Centre (50°52'48"N 4°42'0" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Leuven Casa Blanca, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Casa Blanca neighbourhood Leuven (50°52'48"N, 4°43'48"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Leuven Casa Blanca, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of the Casa Blanca Neighbourhood Leuven (50°52'48"N 4°43'48"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Uccle KMI, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Uccle KMI (50°47'49"N 4°21'29" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Leuven City centre, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of city centre of Leuven (50°52'48"N, 4°42'0"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</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>
Field Survey of Wireless M-Bus Encryption for Energy Metering Applications in Residential Buildings
<p>This is the pseudonymized data of the paper "Field Survey of Wireless M-Bus Encryption for Energy Metering Applications in Residential Buildings" by Hiller v. Gärtringen et al. 2024.</p> <p>Each entry represents a unique wireless M-Bus device that was captured during our field study.</p> <p>Manufacturers and serial numbers are mapped to new identifiers.<br>Payload was removed.</p> <p>The meaning of the columns in the data set are:</p> <table> <tbody> <tr> <td><strong>name</strong></td> <td><strong>type and manifestations</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>id</td> <td>integer</td> <td> <p>Unique for each wireless transmitting device.<br>Counting up from 1 to n of devices.</p> </td> </tr> <tr> <td>manufacturer</td> <td> <p>enumeration</p> <ul> <li>MAN1 - MAN16</li> </ul> </td> <td>Pseudonymized manufacturer identifier.</td> </tr> <tr> <td>device type</td> <td> <p>enumeration</p> <ul> <li>heat cost allocator</li> <li>heat meter</li> <li>temperature or humidity sensor</li> <li>warm water meter</li> <li>water meter</li> <li>radio control device</li> <li>smoke detector</li> <li>unknown type</li> </ul> </td> <td>Device types are described in EN 13757-7 Table 13</td> </tr> <tr> <td>number of telegrams</td> <td>integer</td> <td>Number of telegrams received from the device.</td> </tr> <tr> <td>has DLL Encryption</td> <td>boolean</td> <td>Indicating, if the device uses DLL encryption.</td> </tr> <tr> <td>AES mode</td> <td> <p>enumeration</p> <ul> <li>not encrypted (mode 0)</li> <li>AES-CBC static key (mode 5)</li> <li>AES-CBC dynamic key (mode 7)</li> <li>AES-CCM (mode 10)</li> </ul> </td> <td>Indicates the AES encryption mode.</td> </tr> <tr> <td>detected in 2022</td> <td>boolean</td> <td> <p>Indicates if the device was detected in the given year.<br>If detected in 2022 and 2023, both are 1.</p> </td> </tr> <tr> <td>detected in 2023</td> <td>boolean</td> <td> <p>Indicates if the device was detected in the given year.<br>If detected in 2022 and 2023, both are 1.</p> </td> </tr> <tr> <td>interpretable</td> <td>boolean</td> <td> <p>Indicates whether we identified the message as interpretable.<br>For a detailed description, see the paper.</p> </td> </tr> </tbody> </table> <p> </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.