This project is part of a multi-lab effort with Pacific Northwest National Laboratory (PNNL), Lawrence Berkeley National Laboratory (LBNL), and the National Renewable Energy Laboratory (NREL) in the area of adaptive and autonomous controls. In commercial buildings, it is estimated that annual building energy use can be cut by an average of 29%, or ~4-5% of overall national energy consumption through corrections to existing buildings controls infrastructure and resulting improvements to operating efficiency.1 Algorithms developed to perform automated fault detection and diagnostics (AFDD) use building operational data to identify the presence of faults and isolate their root causes. As a result, AFDD techniques are necessary in determining when corrections to building operations are needed and are a critical feature of adaptive and autonomous controls. The elimination of rules-based algorithms is an active area of research in buildings controls. As buildings become more data rich, analytics to improve the models developed for identifying and calculating faults, as well as the overall accuracy of these FDD solutions and their resulting energy efficiency, is of increasing relevance to the buildings community. Approaches investigated include first-principle or white-box (model-driven), black-box (data-driven), or gray-box (i.e. a combination of physical and empirical models). The most effective approach will depend in part on the building type, existing automation infrastructure, and type of fault under investigation. For heating, ventilation, and air conditioning (HVAC) systems, for example, BTO aims to achieve 20% end-use energy savings through research and development of novel adaptive and autonomous control solutions. This comprehensive multi-lab core effort aims to contribute to this goal through tasks that include the research and development of early-stage AFDD approaches for both buildings with and without existing automation systems, exploration of more effective integration schemes of sensor and metering data to improve the accuracy of models and the utilization of fewer sensors than are required by traditional rule-based methods, and data curation along with performance testing and benchmarking for field testing and verification of the approaches developed through this effort, as well as by and for researchers and members of the buildings community. As part of this multi-lab effort, Oak Ridge National Laboratory (ORNL) will pursue two core activities.
ORNL will collaborate with PNNL on the exploration of adaptive supervisory control to capture the changes in buildings loads and equipment dynamics and mathematical methods for adjusting the operation of the HVAC system with a target of >15% energy consumption reduction relative to a field representative baseline as a foundation for application of adaptive control to other types of building systems. In addition, through the Flexible Research Platforms (FRPs), ORNL will conduct field testing and verification of the novel model-based AFDD platform NREL will develop that leverages whole-building, physics-based energy models to provide fault detection for small- to medium-sized commercial buildings (up to 50,000 square feet), which are limited in their options due to the typical lack of existing building automation infrastructure. Data from the FRPs will be used to validate the models developed.
DOE Technology Manager: Marina Sofos
Lead Performer: Teja Kuruganti, Oak Ridge National Laboratory
1 N. Fernandez et al. Impacts of Commercial Building Controls on Energy Savings and Peak Load Reduction. PNNL Report 2017. http://buildingretuning.pnnl.gov/publications/PNNL-25985.pdf