A labeled dataset for building HVAC systems operating in faulted and fault-free states

被引:14
作者
Granderson, Jessica [1 ]
Lin, Guanjing [1 ]
Chen, Yimin [1 ]
Casillas, Armando [1 ]
Wen, Jin [2 ]
Chen, Zhelun [2 ]
Im, Piljae [3 ]
Huang, Sen [3 ]
Ling, Jiazhen [4 ]
机构
[1] Lawrence Berkeley Natl Lab, Berkeley, CA 94720 USA
[2] Drexel Univ, Philadelphia, PA USA
[3] Oak Ridge Natl Lab, Oak Ridge, TN USA
[4] Natl Renewable Energy Lab, Golden, CO USA
关键词
PERFORMANCE; DIAGNOSTICS; ENERGY; PROGNOSTICS; FRAMEWORK; IMPACTS; SENSOR;
D O I
10.1038/s41597-023-02197-w
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:13
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