Feature article: Benchmark Dataset Development and Applications
Benchmark Dataset Development and Applications Tianzhen Hong, Na Luo, Lawrence Berkeley National Laboratory, USA Lieko Earle, National Renewable Energy Laboratory, USA Piljae Im, Oak Ridge National Laboratory, USA Vikas Chandan, Chitra Sivaraman, Pacific Northwest National Laboratory, USA
Introduction Research-grade datasets from real buildings can address essential gaps that limit our present data analytics capabilities. Buildings are responsible for 40% of primary energy consumption in the U.S. (about one third globally). With today’s technologies (e.g., energy efficiency, sensors and advanced controls) there is potential to reduce energy use in buildings by up to 50%. Reducing energy waste in buildings and optimizing building operations require access to a diverse and integrated set of data. However, it is currently time-consuming and hard to find datasets that have adequate data coverage (e.g., indoor and outdoor environmental parameters, occupant parameters, energy end uses, building system operational parameters), good data quality, and clear documentation (e.g., metadata description). In an ideal world these data would be securely collected at little cost with high temporal and spatial fidelity and include every attribute relevant to building performance and occupant comfort. The use cases of such datasets are myriad. They can establish the ground truth of a building’s operation. Correlations in the data can suggest least-cost pathways to accomplishing tasks like nonintrusive load monitoring, virtual sensing, building energy model calibration, forecasting, benchmarking, control optimization, fault detection, and many others. Analyzing down-sampled data (at lower temporal or spatial fidelity) can help us quantify the capabilities and limitations of lower resolution datasets. Of course, measuring ground truth at high resolution in all buildings is impractical. As such, there is a need to collect, curate, and make publicly available high-resolution data from a small number of buildings that have broad applicability to a variety of high-impact use cases. This effort can help determine the level of resolution required for most effectively optimizing building operations through advances in data analytics and control technologies. It can also provide a common, high-quality benchmark against which competing algorithms can be fairly compared. To fulfil such needs, the Building Technologies Office of United States Department of Energy has funded this research project, Benchmark Dataset Development and Applications, which is jointly led by the authors.
Project Goals The project aims to characterize major use cases for building datasets, define an appropriate data infrastructure, inventory existing building datasets to identify resources that can be used/shared, and develop an experimental plan for a subsequent multi-year effort to collect and curate high-
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volume 31 number 2
