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Abstract: Automatic fault detection and diagnosis (FDD) can help enhance building energy efficiency by facilitating early detection of occurrence of system faults, especially those of air-conditioning systems, thus enabling rectification of the faults before much energy is wasted due to such faults. However, building owners may not invest in FDD unless they are convinced of the energy cost savings that can be achieved. This paper presents the results of a study on the energy cost impacts of a range of common system faults in variable air volume (VAV) air-conditioning systems, which are widely adopted for their good part-load energy efficiency. The faults studied include room air temperature sensor offset, stuck VAV box damper, supply air temperature sensor offset, stuck outdoor air damper and stuck/leaking cooling coil valve. The simulation results indicate that some faults may significantly increase energy use in buildings, for example, negative room air temperature sensor offset, stuck open VAV box damper, negative supply air temperature sensor offset, stuck open outdoor air damper and stuck open and leaking cooling coil valve. Since building occupants may adapt to the symptoms of these faults, such as reduced room air temperature, and thus may not complain about them, the occurrence of such faults are not immediately apparent unless a FDD system is available. Some other faults, e.g. positive supply air temperature sensor offset, positive room air temperature sensor offset, stuck closed cooling coil valve and stuck closed VAV box damper, may allow less energy to be used but will lead to unbearable indoor environmental conditions, such as high indoor temperature. Such faults, therefore, can easily be detected even without a FDD system, as there will be feedback from the building occupants. [Copyright &y& Elsevier]

Highlights: [•] A method to evaluate the probability of saving shortfall for energy performance contracting projects is proposed. [•] Two calibrated building energy models are developed using EnergyPlus. [•] The influential parameters affecting the chiller plant energy use are identified. [•] The probability distributions functions are developed using empirical data. [•] A case study is analysed to illustrate the methodology. [Copyright &y& Elsevier]