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Extracted Content

• Heating . 7,200 kBtu/h (2110 kW)

  • Refrigeration . 7,200 kBtu/h (600 tons or 2110 kW)

FDD is a program procedure for identifying and isolating operational flaws in a system. FDD uses data-driven or knowledge-driven techniques. Data-driven techniques include artificial intelligence (AI) and machine learning. Knowledge-driven techniques include having an FDD specialist use qualitative methods to analyze fault scenarios. [84] Refer to the credit language for minimum required FDD software functionality.

Include fault detection algorithms that address at least 60% of the total air handling unit capacity. Additionally, include fault detection algorithms that address at least 60% of the total combined capacity for large commercial refrigeration systems, large hydronic heating systems, and large hydronic cooling systems, where a large system is defined as a system with a total installed capacity exceeding 7,200 kBtu/h (600 tons or 2110 kW).

Faults assessed may include improper economizer or energy recovery operation, faulty sensor readings, improper valve and damper operation, improper equipment schedules, improper operation of control system reset algorithms (e.g., setpoint always at maximum value), nonoptimal zone temperature setpoints (e.g., lower than recommended deadband; same values for occupied and unoccupied setpoints), equipment short cycling, improper chiller and boiler plant lockouts, and unstable/hunting control loop.

84 M. Mirnaghi and F. Haghighat, “Fault Detection and Diagnosis of Large-scale HVAC Systems in Buildings Using Data-driven methods: A Comprehensive Review,” Energy and Buildings 229 (2020), https://doi.org/10.1016/j.enbuild.2020.110492.

U.S. Green Building Council LEED v5 Reference Guide for Operations and Maintenance, April 2025 Launch Edition 188