Energy conservation is a critical issue, particularly in manufacturing industries with intensive energy usage. The proportional–integral–derivative (PID) controller fails to adjust shop floor temperature against indoor heat dynamics which substantially affect the stability and robustness of the system to achieve energy conservation. By jointly considering the make-up unit (MAU) and a set of collaborative dry cooling coil (DCC) units of a large scaled industrial air condition system, an intelligent control model is proposed in the study to achieve energy conservation and ensure stability of shop floor temperature simultaneously. The temperature set point of the MAU, and the openness of massive distributed DCCs in the factory are optimized in real-time to guarantee the comfort of shop floor temperature, and minimize energy usage and damping. A combined support vector regression with genetic algorithm is proposed to solve the problem efficiently. In addition, the energy efficiency and robustness of the proposed optimization model in securing the stability of the shop floor temperature against indoor heat dynamics is proven through a case study. The total energy consumed by the proposed model is reduced by 7.09% compared with PID controller. The model can regulate shop floor temperature to facilitate operator comfort, product quality, and safe working environment.