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7 results for “building energy efficiency”
Hourly U.S. Building Electricity Use, Cost, and Emissions Baselines to Support Time-Sensitive Analyses of Energy Efficiency and Flexibility Measures
<p>These data underpin an analysis of the time-sensitive impacts of energy efficiency and flexibility measures in the U.S. building sector using Scout (<a href="https://scout.energy.gov">scout.energy.gov</a>), a reproducible and granular model of U.S. building energy use developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office.</p> <p>The analysis applies sub-annual adjustments to U.S. baseline building energy use, cost, and emissions in order to characterize how these metrics vary across hour of the day, season, and geographic region in the U.S. building sector. These adjustments are based on daily energy load, price, and emissions shapes from various data sources and are used to re-apportion baseline energy, cost, and emissions totals from <a href="https://www.eia.gov/outlooks/aeo/data/browser/%20/%20%7b%20/%20# \ }/?id=2-AEO2018 \ { \ & \ }cases=r ef2018 \ { \ & \ }sourcekey=0">EIA's Annual Energy Outlook (AEO) Reference Case projections</a> across all hours of a year. The resulting sub-annual baselines are specified by building sector, end use, region, and season and can be used in analyses of building efficiency and flexibility measures to quantify their time-sensitive impacts at the national scale. Analyses of these data demonstrate that energy efficiency measures continue to show strong value under a time-sensitive framework while the value of flexibility depends on assumed electricity rates, measure magnitude and duration, and the amount of savings already captured by efficiency.</p> <p>The data uploaded below include CSV files that show hourly energy use, cost, and emissions totals for the U.S. building sector as well as by end-use, region, and season. An additional CSV includes residential and commercial price intensities (USD/quad) for all hours of the day based on different time-of-use (TOU) rate data from the U.S. Utility Rate Database (URDB). Further detail on each of these CSVs is given below:</p> <ul> <li>'TSV_baseline_totals.csv': this file shows hourly total energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030. It presents these estimates in Quads (source), Quads (site), and TWh (site). For the cost totals, it presents two estimates for each year and building sector, including one using the median TOU rate from the URDB and one using the average retail rate for the corresponding building sector. For converting source energy to site, total delivered electricity and electricity-related losses data for the residential and commercial sector are drawn from <a href="https://www.eia.gov/outlooks/aeo/data/browser/#/?id=2-AEO2018&sourcekey=0">AEO Summary Table A2</a>.</li> <li>'TSV_baseline_end-use.csv': this file shows hourly energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030 broken out by building end-use. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.</li> <li>'TSV_baseline_region.csv': this file shows hourly energy, cost, and emissions estimates for commercial and residential space heating and cooling end uses in 2018 and 2030 for each <a href="https://www.eia.gov/consumption/residential/maps.php">American Institute of Architects (AIA) climate zone</a>. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.</li> <li>'TSV_baseline_region_season.csv': this file shows a similar disaggregation of the data as ‘TSV_baseline_region.csv’, but it further disaggregates results by season. The seasonal definitions are as follows: 'intermediate' (October to November; March to April), 'winter' (November to February), and 'summer' (May to September).</li> <li>'TSV_annual_price_intensities.csv': this file presents annual hourly price intensities for the commercial and residential building sectors in 2018 and 2030 based on different TOU rate data from the URDB. Three different rate structures are included for each building sector, and these are the 5th, 50th, and 95th percentile of all existing commercial and residential TOU rates in the URDB in terms of their peak to off-peak price ratio.</li> </ul>
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>
Dataset supporting publication: 'GEOFIT: Ground source heat pump systems for energy efficient building retrofitting'
<p>Dataset supporting publication: Presentation of GEOFIT at the <a href="https://www.hp-summit.de/en">European Heat Pump Summit</a> (2019) </p> <p>Available for download: <a href="https://zenodo.org/record/3901527">GEOFIT Zenodo</a></p> <p>The integration of geothermal systems for heating / cooling solutions in conjunction with heat pump technology is a major challenge, particularly in the case of renovation. In order to find the best solution for the increased flow temperatures compared to a new building for the renovation, various heat pump configurations are evaluated according to energy and economic criteria. A refrigerant with low greenhouse gas potential (GWP) is used as the working medium, e.g. R1234ze (E)) less than 10 used. A favorable refrigeration circuit configuration for high flow temperatures is a twin-circuit system, which essentially consists of two heat pumps with different condensing temperatures and the same evaporation temperatures. An alternative to this is a single-stage configuration with a significantly larger condenser for improved supercooling. Both systems should enable efficient operation when renovating buildings.</p>
A three-year building operational performance dataset for informing energy efficiency
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Dataset supporting publication: "Environmental sustainability and Energy Efficiency in Historical Buildings: GeoFit Project Implementation in the Case Study of a medieval fortress in Perugia"
<p>Dataset supporting publication:“Environmental sustainability and Energy Efficiency in Historical Buildings: GeoFit Project Implementation in the Case Study of a medieval fortress in Perugia” (publication available for download: <a href="https://zenodo.org/record/7435513">GEOFIT Zenodo</a>)</p> <p>Italian cities are mainly constituted by buildings constructed until the mid-20th century by pre-industrial construction techniques. A HVAC system for the energy retrofit of historical buildings is evaluated when applied in the case study of Sant’Apollinare. It consists of a ground source heat pump a water tank for thermal energy storage connected to a low-temperature radiant system and air handling unit. The building thermal-energy behavior, typically influenced by thermal inertia in historical buildings, and the novel HVAC system performance interactions are comparatively assessed together with more traditional scenarios. Energy demand decreases by about one third compared to the pre-retrofit situation.</p>
"BUILDING THE FUTURE: ENERGY-EFFICIENT BUILDINGS AND LOW-CARBON TECHNOLOGIES"
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Socio-economic Database for Impact Assessment of Energy Efficiency Measures in Buildings and Supply-side Investments
<p>This upload is a deliverable (D4.2) of the H2020 EERAdata project. It contains the socio-economic data for impact assessment of energy efficiency measures in buildings within three implementing partners - Andalusian Energy Agency (AAE), City of Copenhagen (COP) and Municipality of Velenje (MOV). Future iterations of the database will be uploaded as more data is made available by each implementing partner.</p>
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