In central air conditioning systems, chillers are the main cooling source. Chiller power consumption comprises 60% of the total power consumed by the system. The major parameters affecting chiller efficiency include cooling water pump efficiency, wet bulb temperature, cooling tower fan frequency, compressor high pressure and cooling water inlet temperature. This study focused on the use of neural networks to integrate, train and simulate the parameters to construct power saving modes. To deal with field load change, this study used neural networks and MATLAB to analyze and simulate the data collected from the field sensors to establish effective energy saving modules to adjust cooling tower fan operating frequency, optimize chiller load distribution and reduce chiller power consumption with various loads to achieve energy-saving goals.