Lawrence Berkeley National Laboratory, Berkeley, USA.
Drexel University, Philadelphia, USA.
Sci Data. 2023 Jun 1;10(1):342. doi: 10.1038/s41597-023-02197-w.
Open data is fueling innovation across many fields. In the domain of building science, datasets that can be used to inform the development of operational applications - for example new control algorithms and performance analysis methods - are extremely difficult to come by. This article summarizes the development and content of the largest known public dataset of building system operations in faulted and fault free states. It covers the most common HVAC systems and configurations in commercial buildings, across a range of climates, fault types, and fault severities. The time series points that are contained in the dataset include measurements that are commonly encountered in existing buildings as well as some that are less typical. Simulation tools, experimental test facilities, and in-situ field operation were used to generate the data. To inform more data-hungry algorithms, most of the simulated data cover a year of operation for each fault-severity combination. The data set is a significant expansion of that first published by the lead authors in 2020.
开放数据正在推动许多领域的创新。在建筑科学领域，用于为运营应用程序（例如新的控制算法和性能分析方法）的开发提供信息的数据集极难获得。本文总结了最大已知的公共建筑系统故障和无故障状态运行数据集的开发和内容。它涵盖了商业建筑中最常见的 HVAC 系统和配置，涵盖了多种气候、故障类型和故障严重程度。数据集包含的时间序列点包括现有建筑物中常见的测量值以及一些不太常见的测量值。使用仿真工具、实验测试设施和现场运行来生成数据。为了支持更多数据密集型的算法，大多数模拟数据涵盖每种故障严重程度组合的一年运行时间。该数据集是由主要作者在 2020 年首次发布的数据的重要扩展。
