It is well documented that 15% to 30% of total HVAC energy in commercial buildings is wasted due to faulty operation. Over 30% of all commercial floor space has management systems that could facilitate a “plug-and-play” diagnostic solution, but adoption of existing automated fault detection and diagnostic (AFDD) systems has been very slow in the market.
The key difficulty in implementing AFDD in these commercial buildings are that the systems are unique for each building. This results in one of two outcomes: (1) a "one-size-fits-all" approach is utilized, resulting in low diagnostic accuracy and many false alarms, or (2) many engineering hours are spent performing refinement, resulting in a high cost of implementation.
To facilitate widespread adoption of AFDD for commercial buildings, a solution is required that can utilize the data that are presently available in these buildings to provide low-cost and highly-accurate diagnostics. Additionally, this solution needs to provide "actionable" data for a building operator that facilitates maintenance scheduling and prioritization by providing comfort and energy information, and by isolating the root cause of the faults identified.