Research on global energy optimization of central air conditioning system based on particle swarm optimizer with penalty functions

In modern large buildings, a significant portion of the building’s energy use is used by the central air conditioning system. This study aims to determine how much energy the central air conditioning system uses during operation by modifying the operating parameters of each main piece of equipment. First, the energy consumption models of each major equipment are established separately, so as to establish a global energy consumption model. For the problem of high coupling among the equipment, various nonlinear constraints are introduced. An updated particle swarm algorithm is used to find the operating parameters that make the central air conditioning system’s operation globally optimal in order to solve the nonlinear constraint optimization problem. Compared with the ordinary gradient descent algorithm, the particle swarm algorithm finds the global optimal solution more easily. According to simulations, the optimization algorithm can reduce energy use by 20% to 30%.