Optimization of Multi-Parameter Collaborative Operation for Central Air-Conditioning Cold Source System in Super High-Rise Buildings

This paper proposes a hybrid integer optimization method based on the Whale Optimization Algorithm (WOA) for the asymmetric central air conditioning chiller system of a 530-m super high-rise building in Guangzhou. Firstly, a three-hidden-layer multilayer perceptron (MLP) chiller model based on 16,276 sets of measured data and a gradient boosting regression cooling tower model based on 21,369 sets of operating condition data were constructed, achieving high-precision modeling of the energy consumption of all equipment in the chiller system. Secondly, a hybrid encoding strategy of “threshold truncation + continuous relaxation” was proposed to integrate discrete on-off states and continuous operating parameters into WOA, and a three-layer constraint repair mechanism was designed to ensure the physical feasibility of the optimization process and the safe operation of equipment. Verification across three load scenarios—low, medium, and high—showed that the optimized system’s energy efficiency ratio (EER) increased by 15.01%, 12.61%, and 11.86%, respectively, with energy savings of 12.91%, 11.18%, and 10.58%. The annual rolling optimization results showed that the average EER increased from 5.07 to 5.88 (16.1%), with energy savings ranging from 8.59% to 18.92%. Sensitivity analysis indicated that pump quantity is the most influential parameter affecting system energy consumption, with an additional pump reducing it by 1.1%. The optimization method proposed in this paper meets the minute-level real-time scheduling requirements of building automation systems and provides an implementable solution for energy-saving optimization of central air conditioning chiller systems in super high-rise buildings.