A real-world energy management data set from a smart company building for optimization and machine learning
We present a real-world data set obtained from monitoring a smart company building over the course of six years. The data set describes the energy consumption of various sites within the building, energy production via a photovoltaic system and a combined-heat-and-power plant, and the detailed opera...
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| creator | Engel, Jens Castellani, Andrea Wollstadt, Patricia Lanfermann, Felix Schmitt, Thomas Schmitt, Sebastian Fischer, Lydia Limmer, Steffen Luttropp, David Jomrich, Florian Unger, Rene Rodemann, Tobias |
| description | We present a real-world data set obtained from monitoring a smart company
building over the course of six years. The data set describes the energy
consumption of various sites within the building, energy production via a
photovoltaic system and a combined-heat-and-power plant, and the detailed
operation of the heating and cooling system. The data set further contains
measurements from an on-site weather station for the same time period. The
data set covers periods of normal operation before the onset of the
Covid-19-pandemic, periods of reduced operation during, and after, the
pandemic. We describe the recording, processing, and curation strategy to
generate the data set. The data set enables the application of a wide
range of methods in the domain of energy management, including
optimization, modelling, and machine learning to optimize building
operations and reduce costs and carbon emissions. |
| doi_str_mv | 10.5061/dryad.73n5tb363 |
| format | Dataset |
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building over the course of six years. The data set describes the energy
consumption of various sites within the building, energy production via a
photovoltaic system and a combined-heat-and-power plant, and the detailed
operation of the heating and cooling system. The data set further contains
measurements from an on-site weather station for the same time period. The
data set covers periods of normal operation before the onset of the
Covid-19-pandemic, periods of reduced operation during, and after, the
pandemic. We describe the recording, processing, and curation strategy to
generate the data set. The data set enables the application of a wide
range of methods in the domain of energy management, including
optimization, modelling, and machine learning to optimize building
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building over the course of six years. The data set describes the energy
consumption of various sites within the building, energy production via a
photovoltaic system and a combined-heat-and-power plant, and the detailed
operation of the heating and cooling system. The data set further contains
measurements from an on-site weather station for the same time period. The
data set covers periods of normal operation before the onset of the
Covid-19-pandemic, periods of reduced operation during, and after, the
pandemic. We describe the recording, processing, and curation strategy to
generate the data set. The data set enables the application of a wide
range of methods in the domain of energy management, including
optimization, modelling, and machine learning to optimize building
operations and reduce costs and carbon emissions.</description><subject>Electrical engineering</subject><subject>energy management</subject><subject>FOS: Engineering and technology</subject><subject>Industrial research</subject><subject>Machine learning</subject><subject>sensors</subject><subject>smart company</subject><fulltext>true</fulltext><rsrctype>dataset</rsrctype><creationdate>2024</creationdate><recordtype>dataset</recordtype><sourceid>PQ8</sourceid><recordid>eNqVjjsOwjAQRN1QIKCm3Qvkp4hQIwTiAPTWJt4EC3sdbYxQOD0JQvRUM8V8nlLbIk93eVVkRkY06b7kXazLqlyq-wGE0CXPIM4AMUk3gkfGjjxxBIMRYaAIrQQPk_UoEZrge-QR6od1xnIHbRAIfbTevjDawIBsppnmZpnAEQpPqbVatOgG2nx1pbLz6Xq8JPNJYyPpXuy0P-oi1zOu_uDqH275f-MNuxhSwg</recordid><startdate>20241126</startdate><enddate>20241126</enddate><creator>Engel, Jens</creator><creator>Castellani, Andrea</creator><creator>Wollstadt, Patricia</creator><creator>Lanfermann, Felix</creator><creator>Schmitt, Thomas</creator><creator>Schmitt, Sebastian</creator><creator>Fischer, Lydia</creator><creator>Limmer, Steffen</creator><creator>Luttropp, David</creator><creator>Jomrich, Florian</creator><creator>Unger, Rene</creator><creator>Rodemann, Tobias</creator><general>Dryad</general><scope>DYCCY</scope><scope>PQ8</scope><orcidid>https://orcid.org/0000-0003-0476-5978</orcidid></search><sort><creationdate>20241126</creationdate><title>A real-world energy management data set from a smart company building for optimization and machine learning</title><author>Engel, Jens ; Castellani, Andrea ; Wollstadt, Patricia ; Lanfermann, Felix ; Schmitt, Thomas ; Schmitt, Sebastian ; Fischer, Lydia ; Limmer, Steffen ; Luttropp, David ; Jomrich, Florian ; Unger, Rene ; Rodemann, Tobias</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-datacite_primary_10_5061_dryad_73n5tb3633</frbrgroupid><rsrctype>datasets</rsrctype><prefilter>datasets</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Electrical engineering</topic><topic>energy management</topic><topic>FOS: Engineering and technology</topic><topic>Industrial research</topic><topic>Machine learning</topic><topic>sensors</topic><topic>smart company</topic><toplevel>online_resources</toplevel><creatorcontrib>Engel, Jens</creatorcontrib><creatorcontrib>Castellani, Andrea</creatorcontrib><creatorcontrib>Wollstadt, Patricia</creatorcontrib><creatorcontrib>Lanfermann, Felix</creatorcontrib><creatorcontrib>Schmitt, Thomas</creatorcontrib><creatorcontrib>Schmitt, Sebastian</creatorcontrib><creatorcontrib>Fischer, Lydia</creatorcontrib><creatorcontrib>Limmer, Steffen</creatorcontrib><creatorcontrib>Luttropp, David</creatorcontrib><creatorcontrib>Jomrich, Florian</creatorcontrib><creatorcontrib>Unger, Rene</creatorcontrib><creatorcontrib>Rodemann, Tobias</creatorcontrib><collection>DataCite (Open Access)</collection><collection>DataCite</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Engel, Jens</au><au>Castellani, Andrea</au><au>Wollstadt, Patricia</au><au>Lanfermann, Felix</au><au>Schmitt, Thomas</au><au>Schmitt, Sebastian</au><au>Fischer, Lydia</au><au>Limmer, Steffen</au><au>Luttropp, David</au><au>Jomrich, Florian</au><au>Unger, Rene</au><au>Rodemann, Tobias</au><format>book</format><genre>unknown</genre><ristype>DATA</ristype><title>A real-world energy management data set from a smart company building for optimization and machine learning</title><date>2024-11-26</date><risdate>2024</risdate><abstract>We present a real-world data set obtained from monitoring a smart company
building over the course of six years. The data set describes the energy
consumption of various sites within the building, energy production via a
photovoltaic system and a combined-heat-and-power plant, and the detailed
operation of the heating and cooling system. The data set further contains
measurements from an on-site weather station for the same time period. The
data set covers periods of normal operation before the onset of the
Covid-19-pandemic, periods of reduced operation during, and after, the
pandemic. We describe the recording, processing, and curation strategy to
generate the data set. The data set enables the application of a wide
range of methods in the domain of energy management, including
optimization, modelling, and machine learning to optimize building
operations and reduce costs and carbon emissions.</abstract><pub>Dryad</pub><doi>10.5061/dryad.73n5tb363</doi><orcidid>https://orcid.org/0000-0003-0476-5978</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | DOI: 10.5061/dryad.73n5tb363 |
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| language | eng |
| recordid | cdi_datacite_primary_10_5061_dryad_73n5tb363 |
| source | DataCite |
| subjects | Electrical engineering energy management FOS: Engineering and technology Industrial research Machine learning sensors smart company |
| title | A real-world energy management data set from a smart company building for optimization and machine learning |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-22T02%3A31%3A58IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-datacite_PQ8&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=unknown&rft.au=Engel,%20Jens&rft.date=2024-11-26&rft_id=info:doi/10.5061/dryad.73n5tb363&rft_dat=%3Cdatacite_PQ8%3E10_5061_dryad_73n5tb363%3C/datacite_PQ8%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |