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

arXiv: https://arxiv.org/abs/2503.11469 · HTML: https://mlsanity.com/paper/2503.11469/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 14 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning](https://mlsanity.com/paper/2011.06296/) (2020)
- [Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise](https://mlsanity.com/paper/2105.00349/) (2021)
- [Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing](https://mlsanity.com/paper/1902.06828/) (2019)
- [Understanding Concept Identification as Consistent Data Clustering Across Multiple Feature Spaces](https://mlsanity.com/paper/2301.05525/) (2023)
- [Evaluating the Impact of Data Availability on Machine Learning-augmented MPC for a Building Energy Management System](https://mlsanity.com/paper/2407.13308/) (2024)
- [Identification of Energy Management Configuration Concepts from a Set of Pareto-optimal Solutions](https://mlsanity.com/paper/2306.08318/) (2023)
- [Regression-Based Model Error Compensation for Hierarchical MPC Building Energy Management System](https://mlsanity.com/paper/2306.09080/) (2023)
- [Implicit Incorporation of Heuristics in MPC-Based Control of a Hydrogen Plant](https://mlsanity.com/paper/2309.10660/) (2023)

## Built on by (most cited first, 1 of 1)

- [Industrial Energy Disaggregation with Digital Twin-generated Dataset and Efficient Data Augmentation](https://mlsanity.com/paper/2506.20525/) (2025)

## Abstract (arXiv)

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.