Objective - To improve the operating efficiency of commercial heating, ventilating, and air-conditioning (HVAC) systems by 10% to 30% through development and demonstration of the enabling measurement science for detecting faults and control errors in commercial HVAC equipment and systems, and transferring the measurement science to the private sector.
What is the new technical idea? The new idea is to leverage the untapped capabilities of modern building automation and control systems by developing embedded FDD tools that monitor the performance of subsystems and automatically detect faults. Advances in building automation technologies provide the prospect of very data–rich performance surveillance in buildings that can be applied on system-wide scales that are necessary to optimize overall system performance and achieve the goal of zero energy consumption. The key to realizing efficiency improvements is combining new measurement technology and performance metrics with analysis techniques that can be implemented in building automation and control products. The resulting systems would have a distributed, embedded intelligence that can detect and respond to faults and operational errors and inefficiencies. Microcontroller technology has advanced in a way that simplifies the implementation of the proposed embedded intelligence. Success is likely because of increasing industry demand for the technology as a way to reduce energy consumption and associated environmental impacts.
What is the research plan? NIST will develop and demonstrate a comprehensive set of tools and algorithms for detecting faults and control errors in a wide range of commercial HVAC equipment and systems following a sequence of steps that has proven successful in the past:
- Develop a rapid prototyping platform for initial development of FDD algorithms that includes a Matlab interface to HVACSIM+ (a sophisticated system simulation package developed at NIST)
- Obtain building automation system data from real buildings and laboratory studies of normal and faulty operation
- Test rapid prototypes using collected data and simulations
- Develop an FDD expert system framework ("FDD Expert Assistant" or FDD-EA) combining equipment-level FDD algorithms with new learning algorithms that adapt to operator feedback, improving FDD reliability and acceptance by minimizing false alarms and helping operators cope with cascades of true alarms
- Conduct field tests in cooperation with existing and anticipated industry CRADA partners
In past years the project has focused on the early steps of this plan and resulted in several equipment specific FDD tools. The current focus has shifted to refinement and testing of a prototype FDD-EA as a means to make those tools more autonomous and more adaptive. Autonomy means tools that are less dependent on a human expert intervening in order to infer critical FDD parameter values from the available data. Adaptation means the tool improves its reliability by "learning" from its mistakes, through interaction and feedback from building operators, a capability that enhances FDD acceptance.NIST will leverage existing CRADAs with industry partners to speed commercialization of FDD research results. NIST will also leverage academic ties to sponsor components of the research through contracts and grant solicitations. The outcome will be a suite of proven algorithms for FDD that can be embedded in hardware that is practical for commercial adaptation.