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84 results for “energy efficiency”
PTR-ToF-MS data from cooking experiments in Healthy Energy-efficient Urban Home Ventilation
<pre>The dataset contains high-resolution PTR-Tof MS data from preparing meals consisting of fried salmon and vegetables in SINTEFs ventilation laboratory. <br>The data are organized in csv files containing concatenated results of ppb-values. PTR-ToF-MS grouped by month, m/z-valuens in column names. Relatable to the list of experiments. See readme file for details and 10.1016/j.buildenv.2024.111743 for description</pre>
ASHRAE 1836-RP main list of energy efficiency measures
<p>Energy Efficiency Measures (EEMs) play a central role throughout the building energy efficiency industry, and lists of EEMs therefore exist in a variety of resources. However, each of these use different conventions for describing and organizing measures, which presents a major challenge for aggregating information across these resources. The ASHRAE 1836-RP main list of energy efficiency measures was assembled as part of ASHRAE Research Project 1836 in order to discover trends in how existing resources describe and organize EEMs. Analysis of this dataset supported the overall objective of 1836-RP, which was to develop a standardized system for the categorization and characterization of EEMs.</p> <p>The dataset contains the complete list of 3,490 EEMs assembled and analyzed as part of 1836-RP. The EEMs were collected from 16 different source documents during the 1836-RP literature review from September 2019 through July 2020. An initial list of suggested sources was provided by the members of the 1836-RP Project Advisory Board, and additional documents were added through the authors’ literature review.</p> <p>A data dictionary can be found in the README.txt file. Additional information on working with this dataset can be found in the project repository: <a href="https://github.com/retrofit-lab/ashrae-1836-rp-text-mining">https://github.com/retrofit-lab/ashrae-1836-rp-text-mining</a></p>
PhytoNode Upgraded: Energy-Efficient Long-Term Environmental Monitoring Using Phytosensing
<p>The urban population continues to grow despite health risks associated with densely populated cities, such as traffic congestion and air pollution. At the same time cities are also further heating up due to climate change. Environmental monitoring is increasingly critical to react quickly to temporarily increased concentrations of, for example, carbon monoxide, nitrogen oxides, ozone, and particulate matter. <br>We introduce a significantly improved version of our PhytoNode, an energy-efficient sensor node designed for phytosensing, that is, using of plants as environmental sensors. We aim for a scalable and sustainable real-time monitoring solution following our vision of an `intelligent plant' as an inexpensive and accurate sensor node. <br>We measure electrical potentials and leaf temperatures of plants to assess their well-being and, in turn, environmental conditions. <br>The PhytoNode achieves long-term energy autonomy by harvesting energy via solar cells and shares data via Bluetooth Low Energy (BLE) communication. We process the gathered time series plant data onboard in real-time using methods of Machine Learning (ML) to analyze the plant's activity and to detect dangerous concentrations of gases. In a few showcasing experiments, we demonstrate the feasibility of both our hardware and software approach for continuous, long-term environmental monitoring based on phytosensing. By embedding engineered devices in living plants as a `plant wearable' that listens to plant responses, we hope to help pushing towards smarter future cities and healthier urban environments. </p> <p> </p> <p>Data repository for our paper "PhytoNode Upgraded: Energy-Efficient Long-Term Environmental Monitoring Using Phytosensing", submitted to the 8th Future of Information and Communication Conference 2025 (FICC 2025). Please refer to the paper for more information.</p>
(PRE) Socio-economic and cultural dataset in relation to Persuasive Strategies to boost Energy Efficiency and in the UK, Spain, Greece and Austria
<p>The dataset has been created from obtaining answers from 303 participants of four different countries in the EU (the questionnaire can be studied in <strong>GreenSoul_Questionnaire.pdf</strong>). It is composed by several factors which are explained in different TXT files. All these factors are contained in a "<strong>all_code_final_zenodo.xlsx</strong>" along with their answers by participants. In the following a short descrition of each TXT which explian the dataset is provided.:</p> <p> * <strong>socio-economic_description_not_dependent_of_work</strong><br> - Contains all the information from participants which is irrespective of their current workplace. This file contains typical socio-demographic and cultural attributes from respondents.</p> <p> * <strong>socio-economic_description_dependent_of_work</strong><br> - Contains socio-economic and cultural information from participants which is relevant to the workplace in relation to energy efficient practices in such environment.</p> <p> * <strong>actions-at-work</strong><br> - Are a set of attributes which describe certain practices of employees in relation to energy efficiency.</p> <p> * <strong>persuasive_strategies</strong><br> - Explain the ratings from 1 to 5 that participants attributed to a set of persuasive strategies. These strategies are framed within Phychological Persuasive principles which are also explained in the file.</p> <p> * <strong>all_attributes_together</strong><br> - All the variables together without distinction of the environment where they are applicable.</p> <p>Finally, plots from every construct or attribute are provided in a zip file (<strong>plots_descriptive_analysis_per_city.zip</strong>) which contains the plots uploaded in "PNG" extension</p>
Role of energy migration in the efficiency of upconversion-based resonance energy transfer to organic acceptors
<p>Graphs, data set and algorithms (in Matlab) for the article:</p> <div>Kotulska, A. M., Prorok, K., Bezkrovnyi, O., Pilch-Wrobel, A., & Bednarkiewicz, A. (2024). Role of energy migration in the efficiency of upconversion-based resonance energy transfer to organic acceptors. <em>Journal of Luminescence</em>, <em>275</em>, 120823. https://doi.org/10.1016/J.JLUMIN.2024.120823</div> <p>(https://www.sciencedirect.com/science/article/pii/S0022231324003879)<br>Abstract: Lanthanide (Ln)-doped upconverting nanocrystals (LnNPs) exhibit suitable features as energy donors for Förster resonance energy transfer (FRET). The sensitivity of biosensors can be improved by optically active materials with anti-Stokes emission, narrowband absorption and emission spectral lines, and long luminescence lifetimes. In contrast to energy reabsorption, energy transfer between the upconversion nanocrystals (UCNPs) and organic dyes attached to their surface can be observed through donor emission quenching and acceptor emission and decreases in the luminescence lifetimes of donors. Although the emission spectra confirmed that FRET occurred from the Er3+ ions to the Rose Bengal acceptor, the luminescence lifetimes were generally not affected by the presence of the acceptor. The Ln3+ dopant in LnNPs, which typically has 20–100 % Yb3+ sensitizer ions and 0.2–2% activator (Er3+/Tm3+/Ho3+) ions, results in hundreds to thousands of Ln3+ ions in a single UCNP. The interaction between multiple Ln3+ ions results in significant energy migration and storage in the Yb3+ sensitizer network, which is often recharged with the energy of the Er3+ ions when they emit and nonradiatively transfer their energy to acceptor species. However, the energy transfer mechanisms could not be unambiguously determined through spectroscopic data due to the nature the upconversion process. Studies confirmed that the energy migration distance was significantly shortened when the LnNP surface contained acceptors; this affected the energy storage and ‘recharging’ capability of the Yb3+ sensitizer network within the UCNPs. These results provide hints on the future use of LnNP as effective FRET probes, in which the highest possible absorption cross section and possibly lowest dopant concentration should be maintained.<br>Keywords: Nanocrystals; Resonance energy transfer; FRET; Monte Carlo; Lanthanide ions</p>
(POST) Socio-economic and cultural dataset in relation to Persuasive Strategies to boost Energy Efficiency and in the UK, Spain, Greece and Austria
<p>The dataset has been created from obtaining post-pilot answers from 106 participants of four different countries in the EU (the questionnaire can be studied in <strong>GreenSoul_Validation_Questionnaire-POST.pdf</strong>). It is composed by several factors which are explained in<strong> POST-coding.ods </strong>file. All these factors are contained in: "<strong>POST-results-socio-economic-model.ods</strong>" and "<strong>POST-results-treatments-evaluation.ods</strong>" along with their answers by participants.</p> <p>Finally, we provided a cleaned version of the dataset to study how can a researcher is able to forecast the ranking that a user will give to different persuasion strategies according to user profiles: "<strong>POST-results-ranking-model.ods</strong>"</p>
Benchmark for energy efficient obstacle detection on head mounted wearable for the vision impaired
<p>Here we present a novel benchmark dataset with the associated challenge, that is to detect obstacles based on head-mounted sensors and lightweight wearable devices to assist Blind and Visually Impaired individuals (BVIs) navigate in indoor environments. The challenge encompasses three objectives: (1) as accurately as possible to detect the obstacles on the pathway that likely lead to a collision; (2) as durably as possible on a given amount of battery power for the detection algorithm or model to run; (3) as reliably as possible to compensate natural head turns so nearby objects would not trigger false alarms. The data provided in the benchmark are collected from the following head mounted sensors: (i) nine low-cost ultrasonic sensors; (ii) one high-end ultrasonic sensor with a larger detection range but higher power consumption; (iii) a 9-Degrees of Freedom (DOF) Inertial Measurement Unit (IMU). The resulting dataset consists of more than 188,000 unique sequences obtained from multiple subjects walking in three different indoor scenarios. This benchmark is to facilitate and encourage accurate yet fast obstacle detection solutions that can really benefit BVIs. </p>
Energy efficiency on Philips Lightings products for outdoor lighting
<p>The dataset is a compilation of specifications and performance metrics for different lighting products from Philips Lighting catalogs. It spans various products across different technology types and years, which suggests a focus on the evolution and comparison of lighting efficiency over time.</p> <p>Here are some key points about the dataset:</p> <p>- **Product Information**: Each entry in the `Nombre` column provides specific details about a Philips Lighting product, likely including the model and technical specifications.</p> <p>- **Technology Classification**: The `Tecno` column classifies each product according to its lighting technology, such as LED, CDM, SOX, etc. This allows for analysis across different types of lighting technologies.</p> <p>- **Energy Consumption and Efficiency**: The dataset includes data on energy consumption (`Consumo`) and efficiency (`Efi(lm/w)` and `Efi2`). These metrics are crucial for understanding the energy cost of running the lights and for analyzing improvements in energy efficiency over time. Efi is the calculated energy efficiency from the catalogue data and the Efi2 is the reported energy eficiency.</p> <p>- **Light Output and Quality**: The `Lumens` and `CCT` columns provide information on the brightness and color temperature of the lighting products. This is valuable for assessing the quality and suitability of the light for various applications.</p> <p>- **Economic Considerations**: The `Precio` column, while not filled in for all entries, would give insights into the economic aspect of the lighting products, potentially allowing for cost-benefit analysis.</p> <p>- **Temporal Trends**: The `Año` column indicates the year associated with the product, which can be used to track changes and advancements in lighting technology over time.</p> <p>- **Product Longevity**: The `Vida` column, although unspecified in the dataset preview, would generally relate to the lifespan of the lighting product, an important factor in both consumer choice and sustainability considerations.</p> <p>In summary, this dataset serves as a resource for analyzing Philips Lighting products' performance over time, understanding trends in lighting technology efficiency, and potentially assisting in strategic decisions related to product development, marketing, and sustainability efforts.</p>
Energy Efficiency Technologies Costs - Support File for Data Manipulation Starter Data Kits
<p>This file can be used to manipulate the Energy Efficiency Technologies Costs data for the Starter Data Kits. </p>
Survey questionnaire and results on Structural Barriers to Investment in Energy Efficiency Policies in the Private Rented Sector
<p>The online survey was designed and conducted in the framework of the EU H2020 project ENPOR ("Actions to Mitigate Energy Poverty in the Private Rented Sector). The aim of the survey was to receive statistically sound insights on structural factors that affect the implementation of energy efficiency policies for the alleviation of energy poverty in the European Private Rented Sector. We developed it as an explorative, semi-quantitative, self-completion online questionnaire, using the online tool “EUSurvey”. We performed the online survey among different stakeholders from academia, policy, NGO’s, landlords and tenant associations, etc.</p>
Survey questionnaire and results on Pay-for-Performance (P4P) schemes for energy efficiency measures
<p>The online survey was designed and conducted in the framework of the EU H2020 SENSEI project. The aim of the survey was to identify stakeholders’ perceptions on how P4P programmes could be integrated into the existing EU regulatory and market framework. We developed a semi-quantitative, self-completion online questionnaire, using the online tool “Alchemer”. The online survey collects input from different experts from the field of academia, consultancies, policymaking, and the energy industry. </p> <p>The questionnaire is used by <em>Tzani</em> <em>et al </em>(2022) to investigate how policy developments and adjustments in the EU can facilitate the design of performance-based energy efficiency programmes. The study combines a Strengths, Weaknesses, Opportunities, and Threats framework with an Analytical Hierarchy Process method and a Threats, Opportunities, Weaknesses, and Strengths matrix for the analysis of different stakeholder perceptions and the formulation of policy strategies.</p> <p>If you use this questionnaire in an academic publication, please cite the corresponding article:</p> <p><em>Tzani, D., Exintaveloni, D.S., Stavrakas, V., Flamos, A. </em><em>Devising policy strategies for the deployment of energy efficiency Pay-for-Performance programmes in the European Union</em><em>. </em></p>
PhytoNodes for Environmental Monitoring: Stimulus Classification based on Natural Plant Signals in an Interactive Energy-efficient Bio-hybrid System
<p>Cities worldwide are growing, putting bigger populations at risk due to urban pollution. Environmental monitoring is essential and requires a major paradigm shift. We need green and inexpensive means of measuring at high sensor densities and with high user acceptance. We propose using phytosensing: using natural living plants as sensors. In plant experiments we gather electrophysiological data with sensor nodes. We expose the plant <em>Zamioculcas zamiifolia</em> to five different stimuli: wind, temperature, blue light, red light, or no stimulus. Using that data we train ten different types of artificial neural networks to classify measured time series according to the respective stimulus. We achieve good accuracy and succeed in running trained classifying artificial neural networks online on the microcontroller of our small energy-efficient sensor node. To indicate later possible use cases, we showcase the system by sending a notification to a smartphone application once our continuous signal analysis detects a given stimulus.</p> <p> </p> <p>Data repository for our paper "PhytoNodes for Environmental Monitoring: Stimulus Classification based on<br> Natural Plant Signals in an Interactive Energy-efficient Bio-hybrid System", submitted to the GoodIT conference. Please refer to the paper for more information.</p> <p> </p> <p><strong>Contents of this repository</strong></p> <ul> <li><em>mu_interface:</em> Code for our data collection plant experiments, based on Raspberry Pis and the <a href="http://cybertronica.co/?q=products/phytosensor">Cybertronica phytosensing and phytoactuating system</a>.</li> <li><em>raw_data: </em>The datasets from our plant experiments for the stimuli wind, temperature, red light, blue light, and no stimulus.</li> <li><em>dl-4-tsc:</em> Deep learning framework developed by <a href="https://doi.org/10.1007/s10618-019-00619-1">Fawaz et. al (Deep learning for time series classification: a review)</a> and adapted to our use case. Find the training and testing datasets in the archives folder as well as the trained classifiers in the results folder.</li> <li><em>classification_results.ods: </em>Overview of the results from the deep learning framework (accuracy, precision, recall, training time).</li> <li><em>TFLite_Models: </em>The trained classifiers in TensorFlow Lite Format.</li> <li><em>00_AI_BLE_MeasuringOnlyWind: </em>Source code for classification on STM-based PhytoNodes (using MCDCNN two-class classifier) and Bluetooth communication. The code is written for the STM32WB55 Nucleo board and can be transferred to the dongle.</li> <li><em>zavrsniProjekt_iOS: </em>Source code of the iOS app used to receive data from the STM-based PhytoNodes.</li> <li><em>Watchplant_application_documentation.pdf: </em>Instructions to build and use the iOS app.</li> </ul>
Data from: Energy efficient homes for rodent control across cityscapes
<p>Cities spend millions of dollars on rodent mitigation to reduce public health risks. Despite these efforts, infestations often remain high. Rodents thrive in the built environment in part due to reduced natural predators and the exploitation of garbage. Though sanitation and greenspace are important factors in rodent mitigation, more complex governance and action are needed. Urban rodents are dynamic and commensal in nature, so understanding the influence of prolific urban features, like building attributes, warrants scrutiny and additionally intersects mitigation strategies with stakeholders at a localized level. Here, we model how residential structures' efficiency influences urban rodent populations. To do so, we created an agent-based model using characteristics of urban brown rats and their natural predator, red foxes, based on three distinct neighborhoods in Philadelphia, Pennsylvania. We varied whether retrofitting occurred and its duration as well as the percent of initial energy-efficient homes in each neighborhood. We found that initial housing conditions, retrofitting, and the duration of retrofitting all significantly reduced final rodent populations. However, retrofitting was most effective in reducing rodent populations in neighborhoods with extensive park access and low commercial activity. Additionally, across neighborhoods, single large efficiency initiatives showed greater potential for rodent reduction. Lastly, we show that the costs of large-scale retrofitting schemes are comparable to ten-year public health spending, demonstrating that retrofitting may have the potential to offset near-term costs. Our results showcase how system-view investments in integrated pest management can lead to sustained rodent pest mitigation and advance sustainable development goals, infrastructure innovation (Goal #9), reduced inequalities (Goal #10), and sustainable cities and communities (Goal #11). </p>
Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization
<p>Data for the Wind Energy Science paper "Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization".</p> <p>The data includes a file describing the wind direction distribution. The i<sup>th</sup> probability value corresponds to the probability of the wind coming between direction i and i+1.</p> <p>The other data files, corresponding to the wind farm layouts, provide the x,y coordinates of the wind turbines. </p>
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>
Efficiency and heat transport processes of low-temperature aquifer thermal energy storage systems: new insights from global sensitivity analyses - Supporting Dataset
<p>This dataset contains the files used to substantiate the outcomes of the publication <em>"Efficiency and heat transport processes of low-temperature aquifer thermal energy storage systems: new insights from global sensitivity analyses"</em>. </p> <p>It includes the output of 250 random model realizations of an aquifer thermal energy storage system in a thick productive aquifer (Case 1). It also includes the output of 500 random model realizations of an aquifer thermal energy storage system in a shallow alluvial aquifer (Case 2 part 1 and part 2).</p> <p>If there is interest in generating new output, the datset also includes the model input files for both cases.</p> <p>(Scripts to process the output data or to generate new output data can be found in the corresponding GitHub repository: https://github.com/lukatas/ATES_SensitivityAnalyses.git )</p>
Raw data for High-speed shear mixing: a versatile energy-efficient ultra-fast strategy for solvent-free amine-functionalised solid CO2 adsorbents for direct air capture
<p><strong>Specification of affiliations:</strong></p> <ul> <li>Pavol Suly - Centre of Polymer Systems</li> <li>Barbora Hanulikova - Centre of Polymer Systems</li> <li>Abdulkadir Bozarslan - Centre of Polymer Systems</li> <li>Milan Masar - Centre of Polymer Systems</li> <li>Michal Urbanek - Centre of Polymer Systems</li> <li>Eva Domincova Bergerova - Centre of Polymer Systems</li> <li>Michal Machovsky - Centre of Polymer Systems</li> <li>Ivo Kuritka - Centre of Polymer Systems</li> </ul> <p> </p> <p>Raw data for the research paper. Information on the data collection are described in the manuscript. </p>
THE ROLE OF PROPERTY MANAGEMENT IN PROMOTING ENERGY-EFFICIENT SOLUTIONS FOR RENTALS
<p>Real estate management plays a key role in promoting energy efficient solutions when renting out properties. The purpose of the study is to analyze the impact of management companies on the introduction of energy-efficient technologies to increase the competitiveness of facilities and reduce operating costs. The methodology is based on the analysis of data on the application of modern energy-efficient solutions, including lighting, heating and automation systems in buildings in the Czech Republic. The results showed that the use of such technologies helps to reduce utility costs by 20-40% and increases the attractiveness of facilities for tenants. In conclusion, property management aimed at energy efficiency ensures the achievement of sustainable development and economic benefits for owners and tenants. These measures increase the market value of the properties and extend the lease terms, which strengthens the position in the real estate rental market.</p>
Dataset of the paper "Energy Efficiency Improvement with Reversible Substations for Electrified Transportation Systems"
<p>The dataset refers to the measurement and simulations of the supply system and rolling stock of line 10 B of Metro de Madrid. Simulations have been performed by changing the position of the reversible substation and computing the current flowing in the braking rheostat of the simulated rolling stock. The data refer to the paper "Energy Efficiency Improvement with Reversible Substations for Electrified Transportation Systems" published in "The Open Transportation Journal".</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>
ScienceDex guides
Understand access before you commit
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.