A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning

• URL: http://arxiv.org/abs/2503.11469v1
• Date: Fri, 14 Mar 2025 14:55:22 GMT
• Title: A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning
• Authors: Jens Engel, Andrea Castellani, Patricia Wollstadt, Felix Lanfermann, Thomas Schmitt, Sebastian Schmitt, Lydia Fischer, Steffen Limmer, David Luttropp, Florian Jomrich, René Unger, Tobias Rodemann,
• Abstract summary: We present a large real-world dataset obtained from monitoring a smart company facility over the course of six years, from 2018 to 2023.<n>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.
• Score: 1.5184812527436609
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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