Abstract
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.
| Original language | English |
|---|---|
| Article number | 342 |
| Journal | Scientific Data |
| Volume | 10 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2023 |
Funding
This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Building Technologies Office, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. The authors wish to acknowledge Ravi Gorthala, University of New Haven, for his contribution of in-situ RTU field data to the dataset. We would like to thank Dr. Sungkyun Jung, Oak Ridge National Laboratory, for his contribution of the simulation RTU data to the dataset. In addition, we thank Brian Walker and Erika Gupta (previously) of the Building Technologies Office for their generous support. This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Building Technologies Office, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. The authors wish to acknowledge Ravi Gorthala, University of New Haven, for his contribution of in-situ RTU field data to the dataset. We would like to thank Dr. Sungkyun Jung, Oak Ridge National Laboratory, for his contribution of the simulation RTU data to the dataset. In addition, we thank Brian Walker and Erika Gupta (previously) of the Building Technologies Office for their generous support. The simulated datasets were created using HVACSIM+ and an EnergyPlus-Modelica co-simulation. HVACSIM + was developed by the US NIST, the Modelica Buildings Library is developed by the Lawrence Berkeley National Laboratory, and EnergyPlus is developed by several contributors through funding from the US Department of Energy. Described with respect to other modeling tools in, HVACSIM+, Modelica, and EnergyPlus are non-proprietary tools to model the behavior of building HVAC systems using physics-based approaches. In addition, Modelon’s air conditioning library was used to model the refrigerant side faults in the RTU system. This library provides ready-to-use refrigeration cycle templates and a wide range of components to create a variety of air conditioning system configurations.