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8 results for “Scenario building”
Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyvaskyla for 2030 and 2050
<p>******************* Please view the README.txt or README.md file for detailed documentation of data. ********************</p> <p>Title: Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyväskylä for 2030 and 2050</p> <p>Date of release: 25/11/2020</p> <p>Identifier: 10.5281/zenodo.4275759</p> <p>Permalink: http://dx.doi.org/10.5281/zenodo.4275759</p> <p>Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J.; Ruusunen, M. Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model, <strong><em>Under Review</em></strong>, 2020.</p> <p>Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README.txt and README.md files.</p> <p><br> Contact information: Jari Pulkkinen, University of Oulu, Oulu, Finland, jari.pulkkinen@oulu.fi; Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi<br> </p> <p>Dates of data: 2030, 2050</p> <p>Type of data: Outdoor Temperature</p> <p>Geographic location: Jyväskylä</p> <p>Time resolution: hourly, full year</p> <p>Format: All data is stored in .csv files</p> <p>Number of files: 1 .zip --> 50 files + README.txt + README.md</p> <p>This directory contains the following datasets: A summary of all the files has been compiled and stored in the "README.txt" and "README.md" files</p> <p> </p> <p>Notifications:</p> <p>Contains modified Copernicus Climate Change Service (C3S) information [2018] and modified Finnish Meteorological Institute [2017,2019] information from etsin.fairdata.fi and from Open data repository (https://en.ilmatieteenlaitos.fi/open-data).</p> <p><br> Contains modified Climate One Building information [2019] (reference Lawrie L.K. and Crawley D.B. 2019) and Test Reference Year 2012 (TRY2012) information from Jylhä et al. [2011] and Jylhä et al. [2015] (Energy demand for the heating and cooling of residential houses in Finland in a changing climate).</p> <p>Contains modified Ruosteenoja et al. [2016] information.</p> <p>Other data and information sources are described in README.txt, README.md, references and on the associated publication.</p>
Scenarios of technical and useful ground-source heat pump potential for building heating and cooling in Western Switzerland
<p>This dataset contains an estimation of the useful and technical potential of shallow ground-source heat pumps (GSHPs) for Western Switzerland, at a spatial resolution of 400 x 400 m<sup>2</sup>. The <strong>technical potential</strong> is hereby defined as the maximum energy that could be extracted from GSHP systems in case of their dense deployment, such as to <em>avoid the over-exploitation</em> of the heat capacity of the ground. We consider GSHPs with <em>vertical closed-loop borehole heat exchangers</em> (BHE) installed at depths of 50 - 200 m. The <strong>useful potential</strong> is defined as the potential that could be delivered to building heating and cooling systems via a water-to-water heat pump.</p> <p>The datasets contains future scenarios of heating and cooling demand, space cooling equipment deployment (service sector only) and climate change models and considers the potential use of DHC. The dataset covers around 80,000 property units (parcels) in the Swiss Cantons of Vaud and Geneva, excluding only the areas of the Alps and the Jura mountains.</p> <p>The data package contains information on the available area for GSHP systems, the heating and cooling demand as well as the resulting technical and useful potentials for all simulated scenarios of future cooling demand (200 Monte Carlo runs), for the case of <strong>direct heat supply</strong> (per pixel of 400 x 400 m<sup>2</sup>) as well as for <strong>district heating and cooling</strong> (DHC). In scenarios without DHC (direct heat supply), the results are summarized by pixel of 400 x 400 m<sup>2</sup>. In scenarios with DHC, the results of potentials <em>within</em> DHCs are summarized by DHC (see <em>*_in_dhc.csv</em>) while potentials <em>outside</em> of DHCs are summarized by pixel (see <em>*_outside_dhc.csv</em>).</p> <p>For details on the methodology applied to obtain the results provided in the data package, please refer to the above-mentioned research articles. A description of all files is provided in<em> Dataset documentation.pdf</em> and metadata is provided in <em>Datapackage.json.</em></p>
Empirical datasets for "Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe's residential buildings sector"
<p>This dataset includes the empirical datasets for the manuscript: Andreas Andreou, Panagiotis Fragkos, Faidra Filippidou, Eleftheria Zisarou, Georgios Avgerinopoulos, Robert Pietzcker, Robin Hasse, Ricarda Rosemann, Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe’s residential buildings sector (under review in Environmetal Research Letters). The dataset contains one CSV file with detailed modelling results for the scenarios presented in the manuscript.</p>
Scout Benchmark Scenarios for U.S. Building Energy and CO2 Emissions to 2050
<p><strong>Overview and Intended Use Cases</strong></p> <p>These scenarios establish a range of futures for U.S. buildings sector energy use and CO<sub>2</sub> emissions to 2050 using <a href="https://scout-bto.readthedocs.io/en/latest/">Scout</a>, a reproducible and granular model of U.S. building energy use, emissions, and consumer costs developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office (BTO).</p> <p>Scout benchmark scenario data are suitable for the following example use cases:</p> <ul> <li>Setting high-level policy goals for U.S. buildings sector energy use, electricity demand, and CO<sub>2</sub> emissions over both the near- and long-term (e.g., X% building CO<sub>2</sub> emissions reductions vs. 2005 levels by 2030, Y% reductions vs. 2005 levels by 2050);</li> <li>Exploring the effects of key deployment dynamics driving U.S. buildings sector energy and CO<sub>2</sub> emissions to 2050 that could be affected by policy levers (e.g., raising minimum technology performance levels; improving market penetration of commercially available technologies; accelerating electrification and/or retrofit rates; introducing breakthrough technologies to the market);</li> <li>Determining priority segments (regions, building types, and end use/technology types) and sequencing of U.S. buildings sector energy and CO<sub>2</sub> emissions reductions and/or changes in total consumption by fuel type to 2050 under a given set of assumptions;</li> <li>Identifying the energy and CO<sub>2</sub> impacts or cost effectiveness of specific technologies or operational approaches of interest—in isolation or after considering competition with other measures in a scenario portfolio; and/or</li> <li>Exploring the total cost of deploying different portfolios of building energy efficiency and end-use electrification measures, as well as the total consumer energy cost savings potential of those portfolios. </li> </ul> <p><strong>Scenario Summary</strong></p> <p>A total of 5 scenarios explore total building energy use, CO<sub>2</sub> emissions, and technology and energy costs from 2024–2050 under varying levels of demand-side deployment of building efficiency and electrification measures and parallel decarbonization of buildings’ electricity supply. Narrative descriptions of these scenarios are as follows:</p> <ul> <li><strong>Stated Policies: </strong>Existing policies and regulations (mainly IRA for buildings) lead to modestly accelerated deployment of HPs/HPWHs but not other efficiency measures in the buildings sector. The power sector decarbonizes consistent with a “<a href="https://www.nrel.gov/docs/fy23osti/84916.pdf">Mid-case (with tax credit phaseout)</a>” scenario.</li> <li><strong>Mid: </strong>Policy makers rely mostly on market-based instruments to moderately increase deployment of efficient technology and fuel switching to heat pumps. The power sector decarbonizes consistent with a “Mid-case with 95% Decarbonization by 2050 (without tax credit phaseout)” scenario.</li> <li><strong>High: </strong>Policy makers use both regulations and market-based instruments to dramatically accelerate deployment of high efficiency technologies and fuel switching to heat pumps, though building technologies with breakthrough increases in performance at low cost do not materialize on the market. The power sector decarbonizes consistent with a “<a href="https://www.nrel.gov/docs/fy23osti/84916.pdf">Mid-case with 100% Decarbonization by 2035 (without tax credit phaseout)</a>” scenario.</li> <li><strong>Breakthrough: </strong>Research and innovation breakthroughs lead to market availability of cost-effective, high-performance building technologies by 2030; these, coupled with accelerated deployment of high efficiency technologies and fuel switching to heat pumps, lead to aggressive buildings sector transformation. The power sector decarbonizes consistent with a “<a href="https://www.nrel.gov/docs/fy23osti/84916.pdf">Mid-case with 100% Decarbonization by 2035 (without tax credit phaseout)</a>” scenario.</li> <li><strong>Inefficient Electrification Sensitivity: </strong>Policy makers use regulations and market-based instruments to encourage fuel switching but do not include provisions that require switching to efficient heat pumps, resulting in a substantial amount of switching to inefficient electric resistance heating and water heating technologies. The power sector decarbonizes consistent with a “<a href="https://www.nrel.gov/docs/fy23osti/84916.pdf">Mid-case (with tax credit phaseout)</a>” scenario.</li> </ul> <p>The key input dimensions that are varied to produce the above range of scenarios are as follows:</p> <ul> <li><u>Market-available technology performance range:</u> the energy performance levels of building technologies available for purchase by end use consumers, bounded by a minimum performance “floor” and maximum performance “ceiling”;</li> <li><u>Load electrification rate and efficiency:</u> the rate at which fossil-fired equipment is converted to electric service, and the efficiency level of the electric equipment; </li> <li><u>Early retrofits:</u> the fraction of consumers that choose to replace existing building equipment and/or envelope components before the end of their useful lifetimes; and</li> <li><u>Power grid decarbonization:</u> the annual average CO<sub>2</sub> emissions intensity of the electricity supplied to the buildings sector across the modeled time horizon (2024–2050), resolved by grid region. </li> </ul> <p>Refer to the attached “Scenario_Guide" PDF for further scenario details and results; instructions for reproducing scenario results are available in “Scenario_Execution” XLSX.</p> <p>Results data are reported as an annual time series (2024–2050) at both a national and regional (<a href="https://www.eia.gov/outlooks/aeo/pdf/nerc_map.pdf">EMM grid region</a>) spatial resolution. While not reflected in this dataset, annual time series data may be further translated to a sub-annual, hourly resolution for integration with grid modeling—please contact the authors for more information.</p> <p><strong>What's New in This Version</strong></p> <p><strong><em>Note: v6.1 updates the file ./Results/Results_Summary.xlsx to reflect the latest scenario runs. Please disregard the outdated version of this file that was posted in v6.</em></strong></p> <p>This set of benchmark scenarios provides an update to <a href="../records/8087519">Version 5</a> of the Scout Benchmark Scenarios (June 2023) using the same scenario definitions but an updated set of baseline and measure input data alongside several minor methodological changes. </p> <p>The following scenario features are new in this dataset:</p> <ul> <li>Reference case data and energy use projections updated to <a href="https://www.eia.gov/outlooks/aeo/">AEO 2023</a>, including updates to energy and stock and technology cost, performance, and lifetime data; updated site-source energy conversions, CO2 emissions intensities, and energy prices; and revised peak and take period definitions that are consistent with 2023 EMM projections.</li> <li>Integration of federal and state cost incentives from AEO 2023 (see <a href="https://www.eia.gov/outlooks/aeo/IIF_IRA/pdf/IRA_IIF.pdf">AEO2023 Issues in Focus: Inflation Reduction Act Cases</a> in the AEO2023 for details); these incentives reduce the initial cost of upgrades for applicable measures.</li> <li>Revised method for allocating end use electricity baselines in AEO from census divisions to EMM regions and states by using <a href="https://www.nrel.gov/buildings/end-use-load-profiles.html">End Use Load Profiles</a> (EULP) data. EULP data now also underpin updated, EMM-resolved hourly load baseline shapes.</li> <li>Retail price projections for grid scenarios are updated to match those produced by NREL under the Department of Energy’s DECARB Initiative (these are similar to but differ in slight ways from NREL’s <a href="https://www.nrel.gov/analysis/standard-scenarios.html">Standard Scenarios</a>). Three scenarios are included: <ul> <li><em>Stated Policies</em>: includes moderate estimates for inputs such as technology costs, fuel prices, and demand growth with no nascent technologies and electric sector policies that match current federal laws and regulations (including IRA & BIL); achieves an 88% reduction in building site electricity emissions <em>intensity</em> (Mt CO2/quad site) from 2005 levels by 2050.</li> <li><em>Mid</em>: consistent with<em> Stated Policies</em> except achieves 97% reduction in building site electricity emissions intensity from 2005 levels by 2050.</li> <li><em>High:</em> includes low demand growth projections with advanced inputs for technology costs and allowance of transmission expansion between regions (without limitations based on historical build rates); federal policies are consistent with implemented laws (including IRA & BIL); building electricity is fully decarbonized after 2035.</li> <li>The previous version of the benchmark datasets used retail price data from EIA’s <a href="https://www.eia.gov/outlooks/aeo/">Annual Energy Outlook</a> scenarios.</li> </ul> </li> <li>In contrast to <a href="https://doi.org/10.5281/zenodo.8087519">Version 5</a>, measures in the “best available” measure tier are not deployed with load flexibility features. </li> </ul>
Input data and results of the RECC v2.5 model for the transformation scenarios of the global building stock
<p>This dataset contains the input data and core results of the RECC v2.5 model for the transformation scenarios of the global building stock. For details abou the RECC model, see DOI <a href="https://doi.org/10.1111/jiec.13023" target="_blank" rel="noopener">https://doi.org/10.1111/jiec.13023</a> and the RECC model landing page: <a href="https://www.industrialecology.uni-freiburg.de/odym-recc" target="_blank" rel="noopener">https://www.industrialecology.uni-freiburg.de/odym-recc</a></p> <p>The following data are included in this dataset:</p> <ul> <li>The entire model input database (120 model parameters)</li> <li>The parameters for the sensitivity analysis (8 parameters)</li> <li>The 70 folders with the core results</li> <li>The master classification file RECC_Classifications_Master_V2.0.xlsx</li> <li>The model config file RECC_Config.xlsx</li> <li>The list of scenario configurations RECC_ModelConfig_List.xlsx</li> <li>The result compilation and exporting configuration file RECCv2.5_EXPORT_Combine_Select.xlsx</li> <li>The main result summary file (extracted from the 70 result folders) Results_Extracted_RECCv2.5_10Regs_sep.xlsx</li> <li>The result summary file for comparison with the CRAFT model timber supply RECCv2.5_10Regs_CRAFT_Coupling_SHARE.xlsx</li> <li>The results of the sensitivity analysis: Results_Extracted_RECCv2.5_10Regs_Sensitivity_sep.xlsx</li> </ul> <p>Note that the result folders of the sensitivity analysis are not archived here (too little information in relation to the data volume). They can be requested from the author. The results can also be recreated by running the RECC model with the sensitivity analysis parameters.</p> <p>The model itself is available as Python code from <a href="https://github.com/IndEcol/RECC-ODYM" target="_blank" rel="noopener">https://github.com/IndEcol/RECC-ODYM</a></p>
Vehicular scenario near a building
<p>Measurements were performed in an urban environment with low building density while traveling in a vehicle at an average speed of 30 km/h. This context allows to evaluate how the 5G network responds in suburban or industrial areas where buildings are scarce. Aspects such as coverage, data rate and connection stability are examined to ensure reliable connectivity while traveling in a vehicle in less dense urban environments.</p>
Graphs for indoor building scenarios for IEEE 802.11 Networks
<p>Graph files and the corresponding EPS figures for the IEEE 802.11 indoor building scenarios used in the experiments for the following publications:</p> <p>* Tejedor-Romero, M., Gimenez-Guzman, J. M., Cruz-Piris, L., Herranz-Oliveros, D., & Marsa-Maestre, I. (2024). Optimal channel assignment on dense Wi-Fi networks using Thermodynamic Threshold Accepting. <em>Engineering Science and Technology, an International Journal</em>, <em>57</em>, 101797, https://doi.org/10.1016/j.jestch.2024.101797</p> <p>* J.M. Gimenez-Guzman, I. Marsa-Maestre, L. Cruz-Piris, D. Orden, M. Tejedor-Romero, "IEEE 802.11 graph models", Alexandria Engineering Journal, https://doi.org/10.1016/j.aej.2022.12.016</p>
Data for: Historical earthquake scenarios for the middle strand of the North Anatolian Fault deduced from archeo-damage inventory and building deformation modeling
<p>This dataset is associated to the article "Historical earthquake scenarios for the middle strand of the North Anatolian Fault deduced from archeo-damage inventory and building deformation modeling " published in Seismological Research Letters (<a href="https://pubs.geoscienceworld.org/ssa/srl/article-abstract/doi/10.1785/0220200278/592607/Historical-Earthquake-Scenarios-for-the-Middle?">link</a>).</p> <p>It includes the following:</p> <ul> <li>The annotated photographs of the EAE (Earthquake Archeological Effects) inventoried in Iznik ("EAE_xxx.pdf").</li> <li>The 3D displacement signals used as input for obelisk modeling ("Displacement_xxx").</li> <li>The output obelisk displacement curves and final block shift values relative to base ("Obelisk_block_motion.pdf").</li> </ul>
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