Find research datasets worth reusing

opencc-by-4.0Sep 2020View details →

Residential Building Image Classifier

This model requires TensorFlow 2.3.0 or above.   Classes are:   0 : multi-family 1 : single-family-1-story 2 : single-family-2-or-more-story

opencc-by-4.0Jul 2022View details →

A Google Earth Engine code to analyze residential buildings' real estate values, summer surface thermal anomaly patterns and urban features: a Florence (Italy) case study

<ol> </ol> The layers included in the code were from the study conducted by the research group of CNR-IBE (Institute of BioEconomy of the National Research Council of Italy) and ISPRA (Italian National Institute for Environmental Protection and Research), published by the Sustainability journal (https://doi.org/10.3390/su14148412). Link to the Google Earth Engine (GEE) code (link: <a href="https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002">https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002</a>) You can analyze and visualize the following spatial layers by accessing the GEE link:  <ol> <li>Daytime summer land surface temperature (raster data, horizontal resolution 30 m, from Landsat-8 remote sensing data, years 2015-2019)</li> <li>Surface thermal hot-spot (raster data, horizontal resolution 30 m) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS Pro tool.</li> <li>Surface albedo (raster data, horizontal resolution 10 m, Sentinel-2A remote sensing data, year 2017)</li> <li>Impervious area (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li>Tree cover (raster data, horizontal resolution 10 m, ISPRA data, year 2018)</li> <li>Grassland area (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li>Water bodies (raster data, horizontal resolution 2 m, Geoscopio Platform of Tuscany, year 2016)</li> <li>Sky View Factor (raster data, horizontal resolution 1 m, lidar data from the OpenData platform of Florence, year 2016)</li> <li>Buildings' units of Florence (shapefile from the OpenData platform of Florence) include data on the residential real estate value from the Real Estate Market Observatory (OMI) of the National Revenue Agency of Italy (source: https://www1.agenziaentrate.gov.it/servizi/Consultazione/ricerca.htm, accessed on 14 July 2022). Data on the characterization of the buffer area (50 m) surrounding the buildings are included in this shapefile [the names of table attributes are reported in the square brackets]: averaged values of the daytime summer land surface temperature [LST_media], thermal hot-spot pattern [Thermal_cl], mean values of sky view factor [SVF_medio], surface albedo [alb_medio], and average percentage areas of imperviousness [ImperArea%], tree cover [TreeArea%], grassland [GrassArea%] and water bodies [WaterArea%]. </li> </ol> Here attached the .txt file of the GEE code.    E-mail Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it Marco Morabito, CNR-IBE, marco.morabito@cnr.it Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it

opencc-by-4.0Jul 2024View details →

Cost and cost distribution of policy-driven investments in decentralized heating systems in residential buildings in Germany - Supplementary material

This repository holds supplementary material for research concerning the Cost and cost distribution of policy-driven investments in decentralized heating systems in residential buildings in Germany which was published as a working paper as Czock, Berit, Cordelia Frings, and Fabian Arnold. Cost and cost distribution of policy-driven investments in decentralized heating systems in residential buildings in Germany. No. 2024-4. Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI), 2024. and is available at https://www.ewi.uni-koeln.de/de/publikationen/cost-and-cost-distribution-of-policy-driven-investments-in-decentralized-heating-systems-in-residential-buildings-in-germany/ This repository includes the following documents <ol> <li>building list</li> <li>description of technical and economic assumptions</li> <li>detailed description of results</li> </ol> Further material can be made available on request.     <div> Cost and cost distribution of policy-driven investments in decentralized heating systems in residential buildings in Germany - Supplementary material Creators Czock, Berit1 © 2024 by Berit Hanna Czock is licensed under <a href="https://creativecommons.org/licenses/by/4.0/?ref=chooser-v1" target="_blank" rel="license noopener noreferrer"> CC BY 4.0 </a> </div>

opencc-by-4.0May 2023View details →

Data Sources for Archetype-based Energy and Material Use Estimation for the Residential Buildings in Arab Gulf Countries

Dataset Name: Literature Data and Archetype Parameter Sheets for the publication, named Archetype-based Energy and Material Use Estimation for the Residential Buildings in Arab Gulf Countries. Description: This dataset includes Excel sheets containing literature sources and archetypal data on GCC countries' residential dwelling typologies. Files: The following files are included in the dataset: <ul> <li>    [CountryName]_LiteratureSources.xlsx: Excel sheet containing literature sources and references,</li> <li>    [CountryName]_ArchetypeParameters.xlsx: Archetype models' semantic, geometric, and technical data used in the generation of energy models,</li> <li>    [CountryName]_Schedules.xlsx: Excel sheet containing the operation schedules for countries. The sheet is compiled from literature sources and reorganized by expert consensus and given in Designbuilder input format,</li> <li>    [CountryName]_Stock.xlsx: Excel sheet containing additional data on the building stock,</li> <li>    VacantHouses.xlsx: Vacant house rates for the countries, the found articles on the web, literature sources, etc.,</li> </ul> Usage: The dataset is intended for researching and analyzing the GCC countries' residential buildings. The literature sources included in the [CountryName]_LiteratureSources.xlsx and [CountryName]_ArchetypeParameters.xlsx files can be used to verify, support, or reproduce the research findings. License: The dataset is licensed under Creative Commons Attribution 4.0 International. Citation: If you use this dataset in your research, please cite it as follows and contact the corresponding author: Akin, Sahin, Chibuikem Chrysogonus Nwagwu, Niko Heeren, and Edgar Hertwich. 2023. “Archetype-Based Energy and Material Use Estimation for the Residential Buildings in Arab Gulf Countries.” Energy and Buildings 298: 113537. https://doi.org/https://doi.org/10.1016/j.enbuild.2023.113537. Contact: The archetypes' energy models (DesignBuilder or IDF files) can be provided on request. If you have any questions or comments about the dataset, please contact sahin.akin@ntnu.no, the corresponding author.

opencc-by-2.5Jun 2023View details →

Building part specific material inventory dataset for residential buildings in Finland

This dataset contains material volumes (m3), material masses (kg), and material intensities (kg/m2) for representative buildings from 51 residential building cohorts in Finland. These data are presented per material and in total, aggregated on three hierarchical levels on the correspondingly named sheets in the OpenDocument Spreadsheet (ODS) file: the entire building (data_building), distinguished between vertical building levels (data_building_level), and distinguished between building parts (data_building part). Further details on the data are provided on the description sheet in the same file. The cohorts are based on building type, main bearing material, main façade material, and construction decade. Each cohort is represented by one inventoried building, covering the combinations in the tables below. All buildings are located in the city of Vantaa, Finland, with the exception of the 1940s and 1950s houses, which are based on type-planned houses and thus have no specific location. One dwelling house cohorts included in the dataset <table> <tbody> <tr> <th>Bearing material, Facade material</th> <th>1940s</th> <th>1950s</th> <th>1960s</th> <th>1970s</th> <th>1980s</th> <th>1990s</th> <th>2000s</th> <th>2010s</th> </tr> <tr> <td>Wood, Wood</td> <td>D</td> <td>D</td> <td>D</td> <td>T</td> <td>D</td> <td>D</td> <td>D</td> <td>D</td> </tr> <tr> <td>Wood, Brick</td> <td> </td> <td>T</td> <td>T</td> <td>D</td> <td>T</td> <td>T</td> <td>T</td> <td>T</td> </tr> <tr> <td>Brick, Brick</td> <td> </td> <td> </td> <td>D</td> <td>D</td> <td>D*</td> <td>D</td> <td>D</td> <td>D</td> </tr> <tr> <td>Concrete, Concrete</td> <td> </td> <td> </td> <td>D</td> <td>T</td> <td>D</td> <td>D</td> <td>D</td> <td>D</td> </tr> <tr> <td>Concrete, Brick</td> <td> </td> <td> </td> <td>T</td> <td>D</td> <td>T</td> <td>T</td> <td>T</td> <td>T</td> </tr> <tr> <td>Concrete, Wood</td> <td> </td> <td> </td> <td>T</td> <td>T</td> <td>T</td> <td>T</td> <td>T</td> <td>T</td> </tr> </tbody> </table> Block of flats cohorts included in the dataset <table> <tbody> <tr> <th>Bearing material, Facade material</th> <th>1940s</th> <th>1950s</th> <th>1960s</th> <th>1970s</th> <th>1980s</th> <th>1990s</th> <th>2000s</th> <th>2010s</th> </tr> </tbody> <tbody> <tr> <td>Concrete, Concrete</td> <td> </td> <td> </td> <td>D</td> <td>D</td> <td>D</td> <td>D</td> <td>D</td> <td>D</td> </tr> <tr> <td>Concrete, Brick</td> <td> </td> <td> </td> <td>T</td> <td>T</td> <td>T</td> <td>T</td> <td>T</td> <td>T</td> </tr> </tbody> </table> D = Direct record based on construction documents. T = Theoretical variant with alternative façade material based on typical contemporary construction practice. All properties except the cladding and any related materials (e.g. battens) are identical with the corresponding direct record (the record for the same decade with the directly recorded bearing material). * Geometry determined based on construction documents from 1979 due to lack of suitably sized cases dated in the 1980s. Insulation thicknesses adjusted to match the represented decade’s building code. The data are primarily based on digitized construction documents obtained from the Vantaa building inspection authority’s archives through the purchase portal Lupapiste Kauppa (<a href="https://kauppa.lupapiste.fi/">https://kauppa.lupapiste.fi/</a>). The 1940s and 1950s type-planned houses’ drawings are from the National Archives of Finland (<a href="https://astia.narc.fi/">https://astia.narc.fi/</a>). Version notes since 1.0.0: 1.0.2: Added wood facade variants to all one dwelling houses which have concrete as the bearing material. 1.0.1: Changes to the terminology and some minor spelling corrections. The building data remain identical, as do their locations on the sheets.

opencc-by-4.0Feb 2024View details →

Audio recordings and soundscape assessments from the study: "Indoor soundscape assessment: A principal components model of acoustic perception in residential buildings"

<h1>Content</h1> The dataset contains processed audio files employed in a listening test performed at the Here East Audio Lab of the University College London to derive a model of acoustic perception in residential buildings [1]. The study followed a full factorial design by combining five urban environments (Factor A) and four indoor sound scenarios (Factor B). The audio files are available in both B-format (Ambix) and A-format. Furthermore, the component scores of each participant in the three derived perceptual dimensions (i.e., comfort, content, familiarity) are made available, along with the psychoacoustic analysis of 20 binaural recordings, each lasting 1 minute, corresponding to the 20 acoustic scenarios to which the 32 participants were exposed. <h1>Audio files</h1> Factor A (Outdoor sounds) Factor A audio recordings were performed in indoor spaces without audible indoor sound sources and with windows partially opened to different urban contexts in the city of London. Sound material was recorded @24bit/48kHz using a First Order Ambisonics (FOA) microphone (Sennheiser AMBEO VR Mic) positioned at the average listener’s ear height in endfire position, with accompanying portable multi-channel audio recorder (Sound Devices MixPre-10T) with channels 1-4 linked for the FOA setting, together with a sound level meter (NTi Audio XL2), both microphones oriented towards the window openings. By recording outdoor acoustic environments in indoor spaces, the effects of reverberation and window filtering were intrinsically embedded in the collected recordings. Factor B (Indoor sounds) Factor B audio recordings were made in low-noise indoor environments using the equipment described above with both microphones oriented roughly towards the sound source of interest. Combinations of Factors A & B Excluding the two “no added sounds” scenarios, a total of seven audio stimuli were played and combined during the listening tests, as described in [1], resulting in total 20 scenarios where no more than 2 sounds were overlapped. Audio Editing and Processing Audio samples were edited and processed in A format in the Digital Audio Workstation Reaper (Cockos) @24bit/48kHz. The edits were performed in terms of removing extraneous sound events by trimming the audio track and creating the necessary crossfades, in order to bring the audio material as close as possible to the scenario it represented. Audio processing was conducted using the Sennheiser Ambeo plugin to generate the B-format audio files, to be correctly spatialized using a playback system of choice. In the process of conversion to B format, the default Ambisonics Correction Filter was engaged and the Low Cut Filter was switched off, while the Microphone Rotation was set to correct for the endfire position used during the recordings. One-minute excerpts were finally extracted. No further audio editing, nor processing was done. Full details about sound recordings and playback levels used in the experiment are available in [1] and in the supplementary materials. The audio files are intended to be employed in future listening tests. <h1>Psychoacoustic Analysis and Soundscape scores (.xlsx file)</h1> The xlsx file is formatted with a row for each individual participant's component scores per each of the 20 experimental conditions, then includes the psychoacoustic analysis of the 60s binaural recording corresponding to each acoustic condition. Details about the psychoacoustic analyses and component scores derivation are provided in [1] and in the related supplementary material. In the sheet "Legend_Exposure_Conditions", the coding of the 20 conditions is provided. The numbers of the levels for factors A and B refer to Table 1 in [1].   [1] Torresin, S., Albatici, R., Aletta, F., Babich, F., Oberman, T., Siboni, S., & Kang, J. (2020). Indoor soundscape assessment: A principal components model of acoustic perception in residential buildings. Building and Environment, 182, 107152.

opencc-by-4.0Dec 2021View details →

Dataset used in the study "Residential buildings real estate values linked to summer surface thermal anomaly patterns and urban features: the Florence (Italy) case study."

This dataset repository includes eight raster layers (Reference System EPSG:3035 - ETRS89-extended / LAEA Europe), used in the study "Residential buildings real estate values linked to summer surface thermal anomaly patterns and urban features: the Florence (Italy) case study", and obtained by the adaptation of analyses carried out by previous studies (Morabito et al., 2021; Guerri et al., 2021; 2022). Further information regarding the source, study period, and horizontal resolution is available in the attached text file.    References Guerri, G., Crisci, A., Congedo, L., Munafò, M., Morabito, M., 2022. A functional seasonal thermal hot-spot classification: Focus on industrial sites. Science of The Total Environment 806, 151383.<a href="http://https://doi.org/10.1016/j.scitotenv.2021.151383"> https://doi.org/10.1016/j.scitotenv.2021.151383</a>. Guerri, G., Crisci, A., Messeri, A., Congedo, L., Munafò, M., Morabito, M., 2021. Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers. Remote Sensing 13, 538. <a href="https://doi.org/10.3390/rs13030538">https://doi.org/10.3390/rs13030538</a>. Morabito, M., Crisci, A., Guerri, G., Messeri, A., Congedo, L., Munafò, M., 2021. Surface Urban Heat Islands in Italian Metropolitan Cities: Tree Cover and Impervious Surface Influences. Science of The Total Environment 751, 142334. <a href="https://doi.org/10.1016/j.scitotenv.2020.142334">https://doi.org/10.1016/j.scitotenv.2020.142334</a>.

opencc-by-4.0Jul 2022View details →

Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus - data set of indoor temperature and relative humidity

This data supplements the journal article:  Buechler E, Pallin S, Boudreaux P, Stockdale M. Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus. Journal of Building Physics. 2017;41(3):225-246. doi:<a href="https://doi.org/10.1177/1744259117701893">10.1177/1744259117701893</a> Abstract: 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. 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. 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).

openodc-odblJun 2022View details →

Supplementary data: "Open modeling of electricity and heat demand curves for all residential buildings in Germany"

This repository contains supplementary data for the paper <a href="https://doi.org/10.1186/s42162-022-00201-y"> "Open modeling of electricity and heat demand curves for all residential buildings in Germany"</a>. See README.md / README.pdf for further details. Citing Please cite as: 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). Funding The authors thank the Federal Ministry for Economic Affairs and Climate Action for funding the research project eGon (funding code: 03EI1002).

opencc-by-4.0Aug 2024View details →

Field Survey of Wireless M-Bus Encryption for Energy Metering Applications in Residential Buildings

This is the pseudonymized data of the paper "Field Survey of Wireless M-Bus Encryption for Energy Metering Applications in Residential Buildings" by Hiller v. Gärtringen et al. 2024. Each entry represents a unique wireless M-Bus device that was captured during our field study. Manufacturers and serial numbers are mapped to new identifiers. Payload was removed. The meaning of the columns in the data set are: <table> <tbody> <tr> <td>name</td> <td>type and manifestations</td> <td>description</td> </tr> <tr> <td>id</td> <td>integer</td> <td> Unique for each wireless transmitting device. Counting up from 1 to n of devices. </td> </tr> <tr> <td>manufacturer</td> <td> enumeration <ul> <li>MAN1 - MAN16</li> </ul> </td> <td>Pseudonymized manufacturer identifier.</td> </tr> <tr> <td>device type</td> <td> enumeration <ul> <li>heat cost allocator</li> <li>heat meter</li> <li>temperature or humidity sensor</li> <li>warm water meter</li> <li>water meter</li> <li>radio control device</li> <li>smoke detector</li> <li>unknown type</li> </ul> </td> <td>Device types are described in EN 13757-7 Table 13</td> </tr> <tr> <td>number of telegrams</td> <td>integer</td> <td>Number of telegrams received from the device.</td> </tr> <tr> <td>has DLL Encryption</td> <td>boolean</td> <td>Indicating, if the device uses DLL encryption.</td> </tr> <tr> <td>AES mode</td> <td> enumeration <ul> <li>not encrypted (mode 0)</li> <li>AES-CBC static key (mode 5)</li> <li>AES-CBC dynamic key (mode 7)</li> <li>AES-CCM (mode 10)</li> </ul> </td> <td>Indicates the AES encryption mode.</td> </tr> <tr> <td>detected in 2022</td> <td>boolean</td> <td> Indicates if the device was detected in the given year. If detected in 2022 and 2023, both are 1. </td> </tr> <tr> <td>detected in 2023</td> <td>boolean</td> <td> Indicates if the device was detected in the given year. If detected in 2022 and 2023, both are 1. </td> </tr> <tr> <td>interpretable</td> <td>boolean</td> <td> Indicates whether we identified the message as interpretable. For a detailed description, see the paper. </td> </tr> </tbody> </table>

opencc-by-4.0Aug 2024View details →

Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa

Dataset Name: Literature Data and Archetype Parameter Sheets for the publication, named Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa. Description: This dataset includes Excel sheets containing literature sources and archetypal data on Western Asian and North African countries' residential dwelling typologies. As well as Vacancy rates used and simulation results. Files: The following files are included in the dataset: <ul> <li>    [CountryName]_LiteratureSources.xlsx: Excel sheet containing literature sources and references,</li> <li>    [CountryName]_ArchetypeParameters.xlsx: Archetype models' semantic, geometric, and technical data used in the generation of energy models,</li> <li>    VacantHouses.xlsx: Vacant house rates for the countries, the found articles on the web, literature sources, etc.,</li> <li>    Resource Use Results: BuildME Simulation Results</li> </ul> Usage: The dataset is intended for researching and analyzing the Western Asian and North African countries' residential buildings. The literature sources included in the [CountryName]_LiteratureSources.xlsx and [CountryName]_ArchetypeParameters.xlsx files can be used to verify, support, or reproduce the research findings. License: The dataset is licensed under Creative Commons Attribution 4.0 International. Citation: If you use this dataset in your research, please cite it as follows and contact the corresponding author: Akin, Sahin, Aida Eghbali, Chibuikem Chrysogonus Nwagwu, and Edgar Hertwich. 2024. “Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa”  https://doi.org/10.5281/zenodo.13380340. Contact: The archetypes' energy models (DesignBuilder or IDF files) can be provided on request. If you have any questions or comments about the dataset, please contact sahin.akin@ntnu.no, the corresponding author.

openodc-odblJun 2024View details →

ETHOS.BUILDA: Residential Building TABULA Archetype Dataset Germany

<h2>Introduction</h2> This dataset contains all residential buildings in Germany with their construction year, size class, refurbishment state, and <a href="https://episcope.eu/iee-project/tabula/">TABULA archetype</a>. It is a partial dump of the ETHOS.BUILDA database (version v8_20240916). ETHOS.BUILDA is a database containing building-level data for the German building stock. It is based on various data sources that are combined and enriched with machine learning approaches to generate one consistent and complete building dataset.  ETHOS.BUILDA is made available under the <a href="http://opendatacommons.org/licenses/odbl/1.0" target="_blank" rel="noopener">Open Database License (ODbL)</a>. The licenses of the contents of the database depend on the data source. The sources of the building attributes and information on the type of processing that was done to assign the information from the raw data to the building in ETHOS.BUILDA are provided for each individual data point. <h2>Data structure and file overview</h2> Building data is provided per federal state, the files are named according to the <a href="https://en.wikipedia.org/wiki/NUTS_statistical_regions_of_Germany" target="_blank" rel="noopener">NUTS-1</a> region names. The building data has the following fields: <table> <tbody> <tr> <td>field name</td> <td>description</td> </tr> <tr> <td>ID</td> <td>unique identifier of the building</td> </tr> <tr> <td>position</td> <td>location of building centroid in WKT-format, EPSG:3035</td> </tr> <tr> <td>construction_year</td> <td> value: construction year,  source: source of the construction year data, lineage: construction year assignment method </td> </tr> <tr> <td>size_class</td> <td> value: size class of the building,  source: source of the size class data, lineage: size class assignment method </td> </tr> <tr> <td>refurbishment_state</td> <td> value: refurbishment state of the building,  source: source of the refurbishment state data, lineage: refurbishment state assignment method </td> </tr> <tr> <td>tabula_type</td> <td> value: TABULA type of the building,  source: source of the TABULA data, lineage: TABULA type assignment method </td> </tr> </tbody> </table> A mapping of the abbreviations of "source" and "lineage" of individual data points to the descriptions is provided in sources.csv and lineages.csv. There is no entry for the source "v3_model.json", as it refers to the internally trained machine learning model for the respective attribute and not to an external data source. The full footprint polygons from which the centroids are derived and the sources of the footprints are found in the related dataset linked as "is supplemented by". <h2>Acknowledgements</h2> This work was supported by the Helmholtz Association under the program "Energy System Design".  Furthermore, the authors would like to express their gratitude to the Federal Ministry for Economic Affairs and Climate Action (BMWK.IIB4) for providing the necessary resources to conduct this study. Our research was supported by the WAAGE Grant Program (Grant No. 03EI1044/03EE 5031D), and we appreciate their financial assistance.

opencc-by-4.0Oct 2020View details →

Residential Building Occupancy Class Image Classifier

0 : RES1 (single-family) 1 : RES3 (multi-family)

opencc-by-4.0Apr 2024View details →

Cleaned EPC for residential building of Lombardia Region - Cened1.2+

Open the record for dataset details and reuse information.

opencc-by-4.0Nov 2021View details →

Development of strategy for combined smart ventilated window and PCM energy storage control for residential building energy saving

The dataset includes the published data in the article Development of strategy for combined smart ventilated window and PCM energy storage control for residential building energy saving. For the details of the data description please refer to the paper.