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Remote Fault Detection of Building HVAC System Using a Global Optimization Program
Abstract
The energy efficiency of HVAC systems in
buildings often degrades with the passage of
time. Significant engineering time is normally
required to determine the cause(s) of the
degradation. This paper reports a preliminary
investigation of a remote fault detection
method using the global optimization program
Solver® (Frontline Systems, 2000) coupled to
a simplified simulation program, which is a
coding of the ASHRAE 'Simplified Energy
Analysis Procedure' (Knebel, 1983). This
approach uses the simulation program in
conjunction with synthetic measured data to
identify faults in the building operation. This
fault detection approach has successfully
identified all of the faulty parameters with
noise levels of 1%, 3% and 6%. It
successfully detected 117 of 120 faulty
parameters in the presence of 10% noise.
These results are based on acceptable errors
for each parameter defined as ±5°F for Tcl,
±2°F for Tr, ±0.4cfm/sf for Vs, and ±5% for
OA(%). If the acceptable errors for each
parameter are reduced to ±2.5°F for Tcl, ±1°F
for Tr, ±0.2cfm/sf for Vs, and ±2.5% for
OA(%), the number of faults successfully
detected decreases to 118 out of 120 faulty
parameters with 1% noise, 112 out of 120 with
3% noise, 102 out of 120 with 6% noise, and
94 out of 120 with 10% noise that were
introduced into synthetic “measured data” that
was generated with the simulation program.
Citation
Lee, S. U.; Claridge, D. E. (2004). Remote Fault Detection of Building HVAC System Using a Global Optimization Program. Energy Systems Laboratory (http://esl.tamu.edu); Texas A&M University (http://www.tamu.edu). Available electronically from https : / /hdl .handle .net /1969 .1 /4602.