Abstract Over the past decades, virtual meters or sensors have been rapidly developed and widely adopted within a number of different fields especially in the building section, and have enabled many intelligent features that would otherwise not be possible and economical. Ambient wet-bulb temperature is normally measured either by a psychrometer, or a combination of dry-bulb temperature sensor and relative humidity sensor. These physical sensors are notoriously costly and problematic considering their accuracy and reliability. Furthermore, ambient wet-bulb temperature is critical in a water-cooled chiller plant to enhance its holistic energy efficiency through specific control strategies, such as cooling tower temperature relief, condenser water supply temperature reset, and so on. This paper introduces a virtual ambient wet-bulb sensor through a black-box method using one low-cost dry-bulb temperature sensor and local weather data, either typical meteorological year (TMY) or real-time weather data. A control algorithm for the cooling tower fan operation was also developed to calibrate this virtual wet-bulb sensor, thus to minimize its measuring deviation. Two case studies in Beijing, China and Texas, USA were adopted to demonstrate the feasibility of this strategy under different climate scenarios. It shows that the virtual sensor combined with the control algorithm has high potential to improve the accuracy of the ambient wet-bulb temperature, and guarantee the chiller plant performance by the proposed control strategies.