Papers › A Real-World Energy Management Dataset from a Smart Company Building for Optimization...
A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning
Jens Engel, Andrea Castellani, Patricia Wollstadt, Felix Lanfermann, Thomas Schmitt, Sebastian Schmitt, Lydia Fischer, Steffen Limmer, David Luttropp, Florian Jomrich, René Unger, Tobias Rodemann
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.
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