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45 results for “residential data”
Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus - data set of indoor temperature and relative humidity
<p>This data supplements the journal article: </p> <p>Buechler E, Pallin S, Boudreaux P, Stockdale M. Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus. <em>Journal of Building Physics</em>. 2017;41(3):225-246. doi:<a href="https://doi.org/10.1177/1744259117701893">10.1177/1744259117701893</a></p> <p>Abstract:</p> <p>The indoor air temperature and relative humidity in residential buildings significantly affect material moisture durability, heating, ventilation, and air-conditioning system performance, and occupant comfort. Therefore, indoor climate data are generally required to define boundary conditions in numerical models that evaluate envelope durability and equipment performance. However, indoor climate data obtained from field studies are influenced by weather, occupant behavior, and internal loads and are generally unrepresentative of the residential building stock. Likewise, whole-building simulation models typically neglect stochastic variables and yield deterministic results that are applicable to only a single home in a specific climate. The purpose of this study was to probabilistically model homes with the simulation engine EnergyPlus to generate indoor climate data that are widely applicable to residential buildings. Monte Carlo methods were used to perform 840,000 simulations on the Oak Ridge National Laboratory supercomputer (Titan) that accounted for stochastic variation in internal loads, air tightness, home size, and thermostat set points. The Effective Moisture Penetration Depth model was used to consider the effects of moisture buffering. The effects of location and building type on indoor climate were analyzed by evaluating six building types and 14 locations across the United States. The average monthly net indoor moisture supply values were calculated for each climate zone, and the distributions of indoor air temperature and relative humidity conditions were compared with ASHRAE 160 and EN 15026 design conditions. The indoor climate data will be incorporated into an online database tool to aid the building community in designing effective heating, ventilation, and air-conditioning systems and moisture durable building envelopes.</p> <p>This supplemental data set includes the hourly temperature and relative humidity for the 10th, 50th, and 90th percentile simulations for each building type in each climate zone. The column headings are of the following format buildingtype_climatezone_output_percentile.</p> <p>There are six building types, B1 (unfinished basement 1-story), B2 (unfinished basement 2-story), C1 (unvented crawlspace 1-story), C2 (unvented crawlspace 2-story), S1 (slab 1-story), and S2 (slab 2-story).</p>
Supplementary data: "Open modeling of electricity and heat demand curves for all residential buildings in Germany"
<p>This repository contains supplementary data for the paper <a href="https://doi.org/10.1186/s42162-022-00201-y"><em> "Open modeling of electricity and heat demand curves for all residential buildings in Germany"</em></a>.</p> <p>See <em>README.md</em> / <em>README.pdf</em> for further details.</p> <p><strong>Citing</strong></p> <p>Please cite as:</p> <p><em>Büttner, C., Amme, J., Endres, J. et al. Open modeling of electricity and heat demand curves for all residential buildings in Germany. Energy Inform 5 (Suppl 1), 21 (2022).</em></p> <p><strong>Funding</strong></p> <p>The authors thank the Federal Ministry for Economic Affairs and Climate Action for funding the research project eGon (funding code: 03EI1002).</p> <p> </p>
Data Files for "Subsidies for Close Substitutes: Aggregate Demand for Residential Solar Electricity"
<p>Data files for "Subsidies for Close Substitutes: Aggregate Demand for Residential Solar Electricity" [https://doi.org/10.1016/j.euroecorev.2024.104848]. Findings of the paper can be replicated using these data files, along with code at https://github.com/xabajian/AP_Solar/. Please contact Alexander Abajian <xander.abajian@gmail.com> with any questions regarding the enclosed files.</p>
Measured and modeled data of the thermo-radiative and hydrological exhanges of a lawn in a residential area (FluxSAP 2012 campaign)
<p>The dataset includes wind, soil water content and soil temperature data measured in a private home garden collected during the FluxSAP campaign in Nantes in 2012. It also includes equivalent data modeled by the surface model for natural soils and vegetation called ISBA for four sensitivity experiments corresponding to different model configurations.</p>
Data from: Multi-level thresholds of residential and agricultural land use for elk avoidance across the Greater Yellowstone Ecosystem
<p>1. Conversion of land for settlements and agriculture is increasing globally and can influence wildlife space use. However, there is limited research to identify the thresholds of land use change that incur wildlife avoidance, and how these thresholds might vary across levels of selection.</p> <p>2. We evaluated multi-level avoidance thresholds of elk (<em>Cervus canadensis</em>) impacted by residential development and irrigated agriculture across the Greater Yellowstone Ecosystem in Idaho, Montana, and Wyoming. Using GPS data from 765 elk in 21 herds, we estimated habitat selection in relation to development and agriculture at 3 levels (home range selection, within home range selection, and movement path selection). Next, using individual selection covariates and associated measures of land use availability, we used functional-response models to evaluate how selection varied based on availability, and in turn, to estimate avoidance thresholds.</p> <p>3. We found individual and level-specific variation in elk responses to environmental factors. Elk exhibited stronger responses (either selection or avoidance) when selecting home range locations (i.e. second-order selection) than when selecting areas within home ranges (i.e. third-order selection) or selecting movement paths (i.e. fourth order selection). Importantly, elk avoidance of development and agriculture changed as the amount of land in these categories changed. Across all levels of selection, elk exhibited neutral selection for human development at low levels of availability (<1.1–2.2% developed) but avoided areas that were >1.1–2.2% developed. Conversely, elk selected positively for irrigated agriculture at low to moderate levels of availability (<52.0–66.2% agriculture) but exhibited neutral selection in areas that were > 52.0–66.2% agriculture.</p> <p>4. Synthesis and Applications: Elk avoidance of low levels of human development suggests conservation efforts such as restrictions on future development or conservation easements could focus on areas that are still below 2% developed. Additionally, because elk selection was strongest at the landscape scale, conservation actions that are based on information about the overall landscape structure may be most impactful. Our results highlight the importance of understanding variability in wildlife habitat selection at multiple levels, particularly in relation to land use change and highlight how functional response modelling can help inform landscape conservation.</p>
Residential Power and Battery Data
<p><strong>Overview</strong></p> <p>The Residential Power and Battery data is an open-source dataset designed to facilitate the advancement of predictive and optimisation algorithms. It features anonymised, minute-by-minute real-world customer data on energy consumption, solar generation, and battery measurements. This dataset was compiled by SwitchDin and made available through Monash University on the Zenodo platform.</p> <p>Open-sourcing the Residential Power and Battery data offers numerous benefits to researchers, developers, and industry stakeholders. By providing access to comprehensive, real-world data on energy consumption, solar generation, and battery usage, the dataset enables the development of more accurate and efficient algorithms for energy management systems. For example, the dataset could facilitate the development of machine learning models that forecast energy consumption patterns, enabling better demand-side management strategies. These improved algorithms contribute to more effective demand response, grid stability, and renewable energy integration, helping to build a more resilient and sustainable energy future.</p> <p>Furthermore, open-sourcing this dataset fosters collaboration and knowledge sharing among researchers and professionals in the energy sector. By making the data freely available, researchers from various backgrounds and organisations can work together to identify patterns, trends, and innovative solutions to pressing challenges in energy management. This collaborative approach accelerates the pace of innovation, as diverse perspectives can generate novel ideas and methods that might not emerge in isolation. As a result, the open-sourcing of the Residential Power and Battery data has the potential to significantly advance the pursuit of a more efficient, reliable, and environmentally friendly energy landscape.</p> <p><strong>Data Structure</strong></p> <p><code>anonymous_public_power_data.rds</code></p> <ul> <li>utc: The date-time in UTC, formatted as <code>yyyy-mm-dd hh:mm:ss</code>.</li> <li>unit: A categorical label denoting the unique identifier for the unit.</li> <li>metric: A categorical label indicating whether the data point corresponds to load or solar power generation.</li> <li>max: A numerical variable denoting the peak value of load or solar power generation in kilowatts (kW) within a one-minute interval.</li> </ul> <p><code>anonymous_public_power_data_per_unit.zip</code></p> <ul> <li>Same as above, but files are split by units.</li> </ul> <p><code>anonymous_public_battery_data.rds</code></p> <ul> <li>unit: A categorical label denoting the unique identifier for the unit.</li> <li>batt_kwh: A numerical variable representing battery kilowatt-hour rating.</li> <li>batt_p_ch: A numerical variable representing battery charge power rating.</li> <li>batt_p_dch: A numerical variable representing battery discharge power rating.</li> </ul>
Norwegian hourly residential electricity demand data with consumer characteristics during the European energy crisis
<p>This dataset was collected to understand how Norwegian households responded to the electricity price shock due to the European energy crisis. It consists of consumer characteristics and their self-reported responses to the extraordinarily high electricity prices which were collected by a survey of 4,446 consumers. The consumer characteristics contain information about socio-demographics such as income, age, education, number of residents, residence type, residence size, and how conscious the respondents are about their electricity consumption. Furthermore, major electricity-consuming appliances are identified, such as whether the residents have an electric vehicle and how they heat their homes, and if they have a variable electricity tariff. In addition, hourly metered electricity consumption data covering October 2020 to March 2022 from a subset of 1,136 residential consumers of the surveyed households and the total hourly residential electricity consumption per Norwegian bidding area from July 2019 to July 2022as well as the hourly day-ahead electricity prices are included in the dataset. These data are interesting to researchers that aim to gain insight into the electricity consumption behaviour of the residential sector and the impact of different socio-demographic variables.</p> <p>A detailed description is available as a data article in Data in Brief: <a href="https://www.sciencedirect.com/science/article/pii/S2352340923007667">Norwegian hourly residential electricity demand data with consumer characteristics during the European energy crisis - ScienceDirect</a></p> <p>Supplementary figures containing the survey results are available here: <a href="../records/11580541">Supplementary result diagrams from household surveys on implicit demand response (zenodo.org)</a></p> <p>Survey answers in Norwegian are available here: <a href="https://zenodo.org/records/15063303">iFleks-prosjekt: Spørreundersøkelser med husholdninger og næringsliv om forbruksrespons på elektrisitetspriser</a></p>
Data from: Residential development alters behavior, movement, and energetics in an apex predator, the puma
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Data from: Turnover in floral composition explains species diversity and temporal stability in the nectar supply of urban residential gardens
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Data from: Multi-level thresholds of residential and agricultural land use for elk avoidance across the Greater Yellowstone Ecosystem
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Data from: Evaluating vector mosquito occurrence in residential rain barrels in central Illinois
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American Residential Macrosystems - Lawn plant data within residential yards in seven major metropolitan areas, 2012-2015
"In seven major U.S. metropolitan cities (Boston, Baltimore, Los Angeles, Miami, Minneapolis/St. Paul, Salt Lake City, and Phoenix), 1m2 plots were sampled in residential front and backyards, as well as nearby natural areas, in order to evaluate the plant community composition, diversity, and percent cover of plant species. In addition, in Los Angeles and Salt Lake City, full yard plant communities were also sampled for a plant community of the entire yard. "
American Residential Macrosystems - Quantitative Homeowner Survey Data, 2012
"We used a closed-ended survey to study the management practices and values of homeowners in six cities across the U.S. that span major ecological biomes and climatic regions: Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, Minneapolis-St. Paul, MN, Phoenix, AZ, and Salt Lake City, UT"
American Residential Macrosystems - Soil chemistry data within residential yards in six major metropolitan areas, 2012-2013
"In six major U.S. metropolitan cities (Boston, Baltimore, Los Angeles, Miami, Minneapolis/St. Paul, and Phoenix), 1-meter soil cores were collected to evaluate soil microbial carbon and nitrogen cycle processes that are sensitive to land management. Laboratory methods followed those used by Raciti et al. (2011a,b) to measure microbial biomass carbon and nitrogen content, microbial respiration, potential net nitrogen mineralization, potential net nitrification, potential denitrification, and pools of extractable ammonium and nitrate. "
Data for assessment of damage to residential dwellings using artificial neural networks
<p>The data provided and the associated MATLAB code were used to build an Artificial Neural Network Model to capture damage to residential home subjected to tornado events in the State of Missouri. The ANN model utilizes relevant tornado, societal demographic, and structural data to determine a building's resulting damage state from an extreme wind event. </p>
Three years of hourly data from 3021 smart heat meters installed in Danish residential buildings
<p>This dataset includes three years of cleaned hourly data from 3021 commercial smart heat meters installed in Danish residential buildings. The data are screened, interpolated to be equidistant, and missing values were imputed using a weighted moving average combined with a scaling algorithm to obey the data's cumulative trend. The original (anonymised) raw data of 3127 smart heat meters are also provided to increase transparency and reproducibility. Together with the consumption data, contextual information about the construction year, the type of building, and, if available, the energy label for the smart heat meters buildings (for all 3127 buildings of the raw data) are provided. The unique meter ID can link this data to the consumption data. A .pdf document describing the purpose of every data column is given in the folder '01_Data'. </p> <p>Besides this, three figures visualising the z-normalised data in different temporal resolutions are provided. Next to the data and the data visualisation, all code, written in R, used for data processing and extensive technical validation of the data is included.</p> <p>For a more extensive description, we would like to refer to our peer-reviewed open-access article describing the dataset and its processing: <a href="https://doi.org/10.1038/s41597-022-01502-3">https://doi.org/10.1038/s41597-022-01502-3</a>. Please also consider citing this article if you use this dataset. (Schaffer, M., Tvedebrink, T. & Marszal-Pomianowska, A. Three years of hourly data from 3021 smart heat meters installed in Danish residential buildings. Sci Data 9,<strong> </strong>420 (2022). https://doi.org/10.1038/s41597-022-01502-3)</p>
Data from: Multi-scale heterogeneity in vegetation and soil carbon in exurban residential land of southeastern Michigan, USA
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Data from: Influence of oil and gas emissions on ambient atmospheric non-methane hydrocarbons in residential areas of northeastern Colorado
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Data from: Using past planning practice to inform biodiversity net gain in residential developments
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Data for assessment of damage to residential dwellings using artificial neural networks
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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.
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DANDI Archive for NWB datasets
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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.