A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning
Abstract We present a large real-world dataset obtained from monitoring a smart company facility over the course of six years, from 2018 to 2023. The dataset includes energy consumption data from various facility areas and components, energy production data from a photovoltaic system and a combined...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05186-3 |
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| _version_ | 1850270908560703488 |
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| author | Jens Engel Andrea Castellani Patricia Wollstadt Felix Lanfermann Thomas Schmitt Sebastian Schmitt Lydia Fischer Steffen Limmer David Luttropp Florian Jomrich René Unger Tobias Rodemann |
| author_facet | Jens Engel Andrea Castellani Patricia Wollstadt Felix Lanfermann Thomas Schmitt Sebastian Schmitt Lydia Fischer Steffen Limmer David Luttropp Florian Jomrich René Unger Tobias Rodemann |
| author_sort | Jens Engel |
| collection | DOAJ |
| description | Abstract We present a large real-world dataset obtained from monitoring a smart company facility over the course of six years, from 2018 to 2023. The dataset includes energy consumption data from various facility areas and components, energy production data from a photovoltaic system and a combined heat and power plant, operational data from heating and cooling systems, and weather data from an on-site weather station. The measurement sensors installed throughout the facility are organized in a hierarchical metering structure with multiple sub-metering levels, which is reflected in the dataset. The dataset contains measurement data from 72 energy meters, 9 heat meters and a weather station. Both raw and processed data at different processing levels, including labeled issues, is available. In this paper, we describe the data acquisition and post-processing employed to create the dataset. The dataset enables the application of a wide range of methods in the domain of energy management, including optimization, modeling, and machine learning to optimize building operations and reduce costs and carbon emissions. |
| format | Article |
| id | doaj-art-237db98f6c144ba795e18326518dff9c |
| institution | OA Journals |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-237db98f6c144ba795e18326518dff9c2025-08-20T01:52:24ZengNature PortfolioScientific Data2052-44632025-05-0112111910.1038/s41597-025-05186-3A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine LearningJens Engel0Andrea Castellani1Patricia Wollstadt2Felix Lanfermann3Thomas Schmitt4Sebastian Schmitt5Lydia Fischer6Steffen Limmer7David Luttropp8Florian Jomrich9René Unger10Tobias Rodemann11Honda Research Institute Europe GmbHHonda Research Institute Europe GmbHHonda Research Institute Europe GmbHHonda Research Institute Europe GmbHHonda Research Institute Europe GmbHHonda Research Institute Europe GmbHHonda Research Institute Europe GmbHHonda Research Institute Europe GmbHHonda Research Institute Europe GmbHHonda R&D Europe (Deutschland) GmbHEA Systems Dresden GmbHHonda Research Institute Europe GmbHAbstract We present a large real-world dataset obtained from monitoring a smart company facility over the course of six years, from 2018 to 2023. The dataset includes energy consumption data from various facility areas and components, energy production data from a photovoltaic system and a combined heat and power plant, operational data from heating and cooling systems, and weather data from an on-site weather station. The measurement sensors installed throughout the facility are organized in a hierarchical metering structure with multiple sub-metering levels, which is reflected in the dataset. The dataset contains measurement data from 72 energy meters, 9 heat meters and a weather station. Both raw and processed data at different processing levels, including labeled issues, is available. In this paper, we describe the data acquisition and post-processing employed to create the dataset. The dataset enables the application of a wide range of methods in the domain of energy management, including optimization, modeling, and machine learning to optimize building operations and reduce costs and carbon emissions.https://doi.org/10.1038/s41597-025-05186-3 |
| spellingShingle | Jens Engel Andrea Castellani Patricia Wollstadt Felix Lanfermann Thomas Schmitt Sebastian Schmitt Lydia Fischer Steffen Limmer David Luttropp Florian Jomrich René Unger Tobias Rodemann A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning Scientific Data |
| title | A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning |
| title_full | A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning |
| title_fullStr | A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning |
| title_full_unstemmed | A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning |
| title_short | A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning |
| title_sort | real world energy management dataset from a smart company building for optimization and machine learning |
| url | https://doi.org/10.1038/s41597-025-05186-3 |
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