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12 results for “Office Building”
Energy consumption data from an office building, waterpark and warehouse in Slovenia and energy production data from a PV Plan (900KW)
<p>The first dataset included 9-month hourly energy data from a waterpark, a warehouse and high-rise office buildings. The second dataset includes 10-year hourly energy production data from a PV plant (900KW). Both datasets refer to Ljubljana, Slovenia. </p>
DK10 - Multi-apartment building & Office - Herning (Denmark)
<p>Data files for building: DK10 - Multi-apartment building & Office - Herning (Denmark)</p> <p>Languages: Danish, English</p> <p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p> <p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p> <p>The files are organised in the following folders (note that not all files are always provided):</p> <ol> <li>Main data and Results, with: <ol> <li>Neutral data inventory.</li> <li>Neutral results report.</li> <li>Original EPC certificate.</li> </ol> </li> <li>Energy Consumption Data, with: <ol> <li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li> </ol> </li> <li>Drawings <ol> <li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li> </ol> </li> <li>Other Data <ol> <li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li> </ol> </li> <li>Dynamic Model <ol> <li>Data to run a dynamic model of the building, if available.</li> </ol> </li> </ol> <p>The files have been redacted to exclude confidential information. </p>
DK9 - Multi-apartment building & Office - Kibæk (Denmark)
<p>Data files for building: DK9 - Multi-apartment building & Office - Kibæk (Denmark)</p> <p>Languages: Danish, English</p> <p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p> <p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p> <p>The files are organised in the following folders (note that not all files are always provided):</p> <ol> <li>Main data and Results, with: <ol> <li>Neutral data inventory.</li> <li>Neutral results report.</li> <li>Original EPC certificate.</li> </ol> </li> <li>Energy Consumption Data, with: <ol> <li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li> </ol> </li> <li>Drawings <ol> <li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li> </ol> </li> <li>Other Data <ol> <li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li> </ol> </li> <li>Dynamic Model <ol> <li>Data to run a dynamic model of the building, if available.</li> </ol> </li> </ol> <p>The files have been redacted to exclude confidential information. </p>
Dataset of uGIM deployed in an office building
<p>A dataset of sensor and energy data collected from uGIM system in an office building located in Portugal.</p> <p>The dataset has a total of three days (24 hour records for each 10 seconds):</p> <p> - summer day</p> <p> - winter day</p> <p> - cloudy day</p> <p> </p> <p>uGIM related publications:<br> - Gomes, L., Vale, Z., & Corchado, J. M. (2020). Microgrid management system based on a multi-agent approach: An office building pilot. Measurement: Journal of the International Measurement Confederation, 154. <a href="http://doi.org/10.1016/j.measurement.2019.107427">https://doi.org/10.1016/j.measurement.2019.107427</a><br> - Gomes, L., Vale, Z. A., & Corchado, J. M. (2020). Multi-Agent Microgrid Management System for Single-Board Computers: A Case Study on Peer-to-Peer Energy Trading. IEEE Access, 8, 64169–64183. <a href="http://doi.org/10.1109/ACCESS.2020.2985254">https://doi.org/10.1109/ACCESS.2020.2985254</a><br> - Gomes, L. (2020). μGIM - Microgrid intelligen management system based on a multi-agent approach and the active participation of end-users [Universidad de Salamanca]. <a href="http://doi.org/10.14201/gredos.144238">https://doi.org/10.14201/gredos.144238</a><br> - Gomes, L., Spínola, J., Vale, Z., & Corchado, J. M. (2019). Agent-based architecture for demand side management using real-time resources’ priorities and a deterministic optimization algorithm. Journal of Cleaner Production, 241, 118154. <a href="http://doi.org/10.1016/j.jclepro.2019.118154">https://doi.org/10.1016/j.jclepro.2019.118154</a></p> <p> </p> <p><em>(if you used this dataset in your publications, please send us your information so we can add your publication to the list above)</em></p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
Longitudinal dataset of human-building interactions in U.S. offices
<p>One year of longitudinal data (fifteen minute interval) on local thermal conditions, related behaviors, and comfort of twenty-four occupants of a medium-sized office building in Philadelphia, PA.</p> <p><strong><a href="https://zenodo.org/api/files/378e889e-3651-4d7a-8648-6e4170d7db06/langevincodebook.xlsx">langevincodebook.xlsx</a></strong> defines all the variables and their associated column number in the raw data .txt file.</p> <p><strong><a href="https://zenodo.org/api/files/378e889e-3651-4d7a-8648-6e4170d7db06/langevindata.txt.zip">langevindata.txt.zip</a></strong> includes the full one-year data from the study.</p>
Energy consumption data of office building and energy production data of a 185KW PV plant
<p>The dataset includes two-year monitoring data from the energy consumption of and office building located in center of Italy. The building has HVAC system, heat pumps for space heating /cooling (overall 120-140 KW load) and lighting subsystems controlled individually and/or overall by BMS.</p> <p>The building is a part of a small smart-grid which includes a PV plant (180KW). Energy production data are monitored and a two-year dataset is provided as well. </p>
Time Series Measurement Data of Office Building in Jakarta Indonesia - Incoming Transformer of 20 kV | 0.4 kV
<p>Time Series Measurement Data of Office Buildings in Jakarta Indonesia - Incoming Three Transformers of 2 MVA rating (20 kV | 0.4 kV)<br> The data was taken by Standardized Power Quality Analyzer within minutes span data during eight days in 2016. <br> The data consist of Voltage, Current, Active/Reactive/Apparent Power, Power Factor, Total Harmonic Distortion (THD) within three-phase measurement. Related data was also included from with it i.e., calculated data for active power losses, Unbalanced Voltage per phase, efficiency etc</p>
reference image data (primary views) for an office building near LAX
<p>image data compressed by view and season (months 1-2-3, 4-5-6, 7-8-9, 10-11-12)</p> <p>see 10.5281/zenodo.6322418 for source files</p>
reference radiance model and result data for an office building near LAX
<p>radiance scene files and metric results generated from high quality referece images for an office building near LAX.</p> <p><strong>contents of lci_tempate.tar.gz:</strong></p> <p>ALLSKIES: <br> 4294 sky conditions generated by write_skydefs.py<br> based on LAX.epw, all conditions with solar altitude >= 2 degrees, diffnorm > 5 w/m^2</p> <p>INSGEO/: (scene files that can be made into an instance using setup.sh)<br> EXT.rad INT.rad LIT2.rad baseglz.rad site.rad</p> <p>MATERIAL/:<br> all.mat</p> <p>POINTS/: (reference view locations as sensor points)<br> ref_views.pts</p> <p>RAD/:<br> ROIglz.rad big.rad</p> <p>ROI/: (radiance scene polygons describing zones around each point)<br> o1.rad o2.rad z1.rad z2.rad</p> <p>VIEWS/: (reference views)<br> o1ref.vf o1ref_r.vf o2ref.vf o2ref_r.vf z1ref.vf z1ref_r.vf z2ref.vf z2ref_r.vf</p> <p>refs/: (source weather data and model units)<br> lax.epw lax.wea sky_runorder.txt units.txt</p> <p>reference images generated with run_step.sh</p> <p>2phs images generated with 2phase_run_auto.sh</p> <p><strong>contents of ref_data.tar.gz:</strong></p> <p>refimgs/ref_data_view_*_metric.txt (for each view)</p> <p>columns: month day hour dgp illum ugr ugp dgp_t1 dgp_t2 avglum loggcr logpwgcr</p> <p>data generated with the run_step.sh file in lci_tempate.tar.gz for all sky files</p> <p>metrics computed with run_rayt.sh in lci_tempate.tar.gz</p> <p>scripts require python3.6-3.8 on mac os or linux with the following packages installed: raytraverse v1.2.7</p> <p> </p> <p> </p>
Bureaux d'affaires à Montréal - données historiques / Montreal office buildings - historical data
<p>Les données présentées ici ont servi à l’écriture de la monographie : <em>Les bureaux d’affaires à Montréal – historique et état des lieux</em> publiée en juin 2022. La monographie est aussi disponible ici en format PDF.</p> <p>Renseignements concernant l’auteur :</p> <p>Michel Hudon, Saint-Pacôme (Québec), Canada, <a href="mailto:m.hudon51@videotron.ca">m.hudon51@videotron.ca</a></p> <p>Description sommaire :</p> <p>La <strong>monographie</strong> retrace l’historique de la localisation des bureaux d’affaires à Montréal et cherche à en expliquer les moteurs. Afin d’établir un portrait quantitatif autant que qualitatif du phénomène, elle fait un suivi détaillé de l’inventaire des espaces de bureaux, immeuble par immeuble, et estime la progression annuelle de l’espace occupé par les bureaux dans chacun des secteurs géographiques définis.</p> <p>Dans les chapitres 1 à 8, elle se concentre sur le centre-ville, puis dans les chapitres 9 à 11, elle analyse la construction de bureaux et leur décentralisation dans les différentes couronnes de développement commercial qui l’entourent.</p> <p>Chaque chapitre fait d’abord un rappel de l’évolution économique durant la période considérée, puis l’analyse des variables urbanistiques (croissance urbaine et répartition spatiale des diverses fonctions). Vient ensuite l’étude du développement des bureaux proprement dit, secteur par secteur.</p> <p>Cette étude s’inscrit dans les recherches traditionnelles sur la géographie des bureaux d’affaires et vise à une meilleure compréhension de la spécificité montréalaise dans ce domaine tout en fournissant une base statistique à des études plus poussées sur la question.</p> <p>Les <strong>données historiques</strong> compilées touchent toute la région métropolitaine de Montréal sur toute la période historique des débuts de la ville jusqu’au printemps 2022. Elles traitent tous les types d’immeubles accueillant des bureaux mobiles.</p> <p>La banque de données classe les immeubles de bureaux par secteur et sous-secteurs suivant les zones d’analyse habituellement reconnues par le marché et elle les catégorise suivant leurs principales caractéristiques physiques.</p> <p>L’objectif premier de cette banque de données est de préciser pour chaque année et chaque sous-secteur le volume d’espace disponible à l’usage de bureaux d’affaires, qu’il soit ou non occupé. Un tel relevé exhaustif devrait permettre des analyses en relation avec diverses variables économiques.</p> <p>Le fichier principal des immeubles s’accompagne d’un fichier de photos et d’un fichier d’articles de journaux.</p> <p>Date de la collecte de données : de 1984 à 2022</p> <p>Date de production des données : 2022-06-01</p> <p>Date de publication des données : 2022-06-01</p> <p>Localisation géographique de la collecte de données : Montréal (région métropolitaine) détaillée en 14 grands secteurs et 72 sous-secteurs</p> <p>Renseignements concernant les organismes subventionnaires ou commanditaires de cette collecte de données : Les données ont été colligées auprès des différentes firmes immobilières, des propriétaires et gestionnaires d’immeubles, des municipalités, notamment aux Archives de la Ville de Montréal ainsi que dans la presse spécialisée. Elles ont été comparées, vérifiées et normalisées par l’auteur.</p>
Let's talk scalability: The current status of multi-domain thermal comfort models as support tools for the design of office buildings (Dataset v1.2.1)
<p><strong>THE PUBLICATION</strong></p> <p>The data set provided is complementary to the thermal comfort review by Mamulova et al., 2023, titled "<strong>Let's talk scalability: The current status of multi-domain thermal comfort models as support tools for the design of office buildings</strong>": <a href="https://doi.org/10.1016/j.buildenv.2023.110502">Link to full publication</a>. The scoping review examines 77 multi-domain thermal comfort studies and initiates a discussion on model scalability; a model parameter which facilitates the understanding and prediction of thermal comfort conditions in real-world practice.</p> <p><strong>THE DATA</strong></p> <p>This database contains 27 scalability parameters per study which are used to analyse current research practices. For the results, please consult the review publication, as this database only contains raw data. For clarity, a legend of the scalability parameters is provided below.</p> <p><strong>*** PLEASE NOTE ***</strong></p> <p><strong>This data set may be utilised, altered and/or expanded. However, you are kindly asked to cite this data set, the review publication (if applicable) and contact the corresponding author at eugenemamulova@gmail.com. </strong></p> <table> <tbody> <tr> <td><em>Citation</em></td> <td><em>Citation number used in Mamulova et al.,"Multi-Domain Thermal Comfort Models for Office Buildings: Are Current Practices Scalable?", (2023)</em></td> <td><em>E.g. 1</em></td> </tr> <tr> <td><em>First Author</em></td> <td><em>Surname of the main author, for reference purposes only.</em></td> <td><em>E.g. Al-Atrash</em></td> </tr> <tr> <td><em>Publication</em></td> <td><em>Publication year</em></td> <td><em>E.g. 2020</em></td> </tr> <tr> <td><em>Dependent A</em></td> <td><em>List of variables used to measure thermal perception</em></td> <td><em>E.g. Neutral temperature/ Thermal sensation</em></td> </tr> <tr> <td><em>Dependent B</em></td> <td><em>Scale used to measure each dependent variable</em></td> <td> </td> </tr> <tr> <td><em>Interaction A</em></td> <td><em>List of interaction effect(s) included in the explanatory/predictive model(s) </em></td> <td><em>E.g. Thermal and age/ Thermal and acoustical and personality</em></td> </tr> <tr> <td><em>Interaction B</em></td> <td><em>Is/are the effect(s) statistically significant?</em></td> <td><em>E.g. yes/ no/ (unknown)</em></td> </tr> <tr> <td><em>Crossed A</em></td> <td><em>List of crossed effect(s) included in the explanatory/predictive model(s) </em></td> <td><em>E.g. Acoustical/ Personality/ Age</em></td> </tr> <tr> <td><em>Crossed B</em></td> <td> </td> <td><em>*Note: Temperature is a main effect and is not included in the list</em></td> </tr> <tr> <td><em>Explanatory A</em></td> <td><em>Type of explanatory model</em></td> <td><em>E.g. Observation/ Statistical/ N/A</em></td> </tr> <tr> <td><em>Explanatory B</em></td> <td><em>Description of the explanatory model</em></td> <td><em>E.g. Asymptotic General Symmetry Test to check significance of difference in thermal perception between window conditions</em></td> </tr> <tr> <td><em>Predictive A</em></td> <td><em>Does the article include a predictive model?</em></td> <td><em>E.g. yes/ no</em></td> </tr> <tr> <td><em>Predictive B</em></td> <td><em>Type of predictive algorithm</em></td> <td><em>E.g. Logistic regression/ N/A</em></td> </tr> <tr> <td><em>Predictive C</em></td> <td><em>Description or formulation of the predictive model</em></td> <td><em>E.g. Probability of feeling too hot and probability of feeling too cold in relation to sound pressure level</em></td> </tr> <tr> <td><em>Performance</em></td> <td><em>Reported predictive performance</em></td> <td><em>E.g. Accuracy = 80%/ F-score = 0.8/ N/A</em></td> </tr> <tr> <td><em>Location</em></td> <td><em>City in which the measurements take place</em></td> <td><em>E.g. Paris</em></td> </tr> <tr> <td><em>Period</em></td> <td><em>Period over which the measurements take place</em></td> <td><em>E.g. Jan-Feb 2020</em></td> </tr> <tr> <td><em>Start time</em></td> <td><em>Time of day at which the measurements begin</em></td> <td><em>*Note: Time of day is not reported for most field studies. For this reason, time of day is only recorded for laboratory experiements.</em></td> </tr> <tr> <td><em>Study type</em></td> <td><em>Type of building and whether the experimental conditions are controlled by the experiment leader</em></td> <td><em>E.g. Field (controlled)/ Field (uncontrolled)/ Lab (controlled)/ Lab (uncontrolled)</em></td> </tr> <tr> <td><em>Building layout</em></td> <td><em>Building layout</em></td> <td><em>E.g. Laboratory office (LO)/ Laboratory neutral (LN)/ Field office (FO)</em></td> </tr> <tr> <td><em>Exposure</em></td> <td><em>Exposure of the participant, in minutes, to the experimental conditions, excluding preparation time</em></td> <td><em>*Note: Exposure is not reported for most field studies. For this reason, exposure is only recorded for laboratory experiements and is assumed to be longer than 60 minutes.</em></td> </tr> <tr> <td><em>Number of buildings/chambers</em></td> <td><em>Number of different locations used for conducting measurements</em></td> <td><em>E.g. 1</em></td> </tr> <tr> <td><em>Number of participants</em></td> <td><em>Number of individuals who take part in each experiment</em></td> <td><em>*Note: Outliers who are subsequently excluded from the modelling phase are not included.</em></td> </tr> <tr> <td><em>Survey type</em></td> <td><em>Description of the type of survey used for subjective measurements</em></td> <td><em>E.g. Longitudinal questionnaire/ Transverse questionnaire/ N/A</em></td> </tr> <tr> <td><em>Survey content</em></td> <td><em>Are the contents of the survey provided in the article?</em></td> <td><em>E.g. Available/ unavailable</em></td> </tr> <tr> <td><em>Survey source</em></td> <td><em>Is/are the source(s) of the survey items mentioned in the article?</em></td> <td><em>E.g. Available/ unavailable</em></td> </tr> <tr> <td><em>Survey reliability</em></td> <td><em>Is the reliability of the survey items reported in the article?</em></td> <td><em>E.g. Available/ unavailable</em></td> </tr> <tr> <td><em>Survey duration</em></td> <td><em>Is the survey duration reported in the article?</em></td> <td><em>E.g. Available/ unavailable</em></td> </tr> <tr> <td><em>Context A</em></td> <td><em>Overview of the contextual information provided by the authors</em></td> <td><em>E.g. Room layout/ Room dimennsions</em></td> </tr> <tr> <td><em>Context B</em></td> <td><em>Qualitative/quantitative contextual information</em></td> <td><em>E.g. Figure containing room layout/ 3m x 3m x 5m</em></td> </tr> <tr> <td><em>Contextual variables A</em></td> <td><em>List of contextual variable(s) measured by the researchers (see Fig. A.)</em></td> <td><em>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</em></td> </tr> <tr> <td><em>Contextual variables B</em></td> <td><em>Range of values included in the experiment and their respective units.</em></td> <td><em>E.g. figure</em></td> </tr> <tr> <td><em>Social variables A</em></td> <td><em>List of social variable(s) measured by the researchers (see Fig. A.)</em></td> <td><em>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</em></td> </tr> <tr> <td><em>Social variables B</em></td> <td><em>Range of values included in the experiment and their respective units.</em></td> <td><em>E.g. [1,2,3,4,5]</em></td> </tr> <tr> <td><em>Personal variables A</em></td> <td><em>List of contextual variable(s) measured by the researchers (see Fig. A.)</em></td> <td><em>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</em></td> </tr> <tr> <td><em>Personal variables B</em></td> <td><em>Range of values included in the experiment and their respective units.</em></td> <td><em>E.g. [red, blue]</em></td> </tr> <tr> <td><em>Physical variables A</em></td> <td><em>List of physical variable(s) measured by the researchers (see Fig. A.)</em></td> <td><em>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</em></td> </tr> <tr> <td><em>Physical variables B</em></td> <td><em>Range of values included in the experiment and their respective units</em></td> <td><em>E.g. dB(A)</em></td> </tr> <tr> <td><em>Full-factorial</em></td> <td><em>Is/are the experiment(s) full-factorial?</em></td> <td><em>*Note: Uncontrolled field experiments are automatically labelled as fractional factorial.</em></td> </tr> <tr> <td><em>(Participant) Control</em></td> <td><em>Do participants have control over one or more experimental conditions?</em></td> <td><em>E.g. Yes/ No</em></td> </tr> <tr> <td><em>With/between subjects</em></td> <td><em>Are the experimental conditions shared between or within the participants?</em></td> <td><em>E.g. w/ b</em></td> </tr> <tr> <td><em>Fixed variables A</em></td> <td><em>List of variables reported as constant during the measurements</em></td> <td><em>E.g. Relative humidity/ Metabolic rate</em></td> </tr> <tr> <td><em>Fixed variables A</em></td> <td><em>(Range of) values and their respective units.</em></td> <td><em>E.g. 30-40%/ 1.2 met</em></td> </tr> <tr> <td><em>Summary</em></td> <td><em>Description of the research outome (outcome of the explanatory and/or predictive modelling)</em></td> <td><em>E.g. Lack of perceived control has a significant negative effect on neutral temperatures.</em></td> </tr> <tr> <td><em>Evaluation</em></td> <td><em>Are the participants invited to evaluate their experience once the experiment has been completed? </em></td> <td><em>E.g. Yes/ no</em></td> </tr> </tbody> </table> <p>Note: The data in v1.1.0 has not yet been optimised for analytics.</p>
Simulation data for the office cell building energy model with the attached overhang
<p>Simulation data for 729,000 variants of the office cell building model with the overhang attached over the window. The variants are determined by the overhang depth and height, location, presence of obstacles, orientation and cooling and heating set points. The office cell model is described in the manuscript "Predicting the shape of loads for an office cell with an overhang from a small number of building energy simulations".</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.