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

Qi Li, Y. Su, Danhong Zhang, Chenyu Liu, Huajun Zhang, Z. Yan
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Abstract

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%.
基于惩罚函数粒子群优化器的中央空调系统全局能量优化研究
在现代大型建筑中,中央空调系统使用了建筑能源使用的很大一部分。本研究旨在通过修改各主要设备的运行参数来确定中央空调系统在运行过程中的能耗。首先,分别建立各主要设备的能耗模型,从而建立全局能耗模型。针对设备间的高耦合问题,引入了各种非线性约束。为了解决中央空调系统的非线性约束优化问题,采用改进的粒子群算法寻找使中央空调系统运行全局最优的运行参数。与普通梯度下降算法相比,粒子群算法更容易找到全局最优解。仿真结果表明,该优化算法可使能耗降低20% ~ 30%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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