六年来真实建筑能耗数据，助力智能优化与低碳运行。
A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning
- 六年间多层级分项计量，覆盖72个电表、9个热表及气象数据
- 包含原始与处理后数据，支持故障标注与机器学习建模
- 适合能源优化、碳减排研究者使用，实测数据价值高
本文发布了一个来自智能公司建筑的六年真实世界数据集（2018–2023），涵盖不同区域与设备的能耗数据、光伏系统与热电联产机组的发电数据、供暖制冷系统的运行数据，以及现场气象站采集的气象数据。设施内安装的测量传感器采用分层计量结构，体现为多级子计量体系。数据集包含72个电能表、9个热能表和一个气象站的测量数据，提供原始与不同处理级别下的数据，包括带有标签的异常事件数据。本文详细描述了数据采集与后处理流程。该数据集支持能源管理领域的多种方法应用，如优化、建模与机器学习，有助于提升建筑运行效率、降低运营成本与碳排放。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。