摘要
本文以系统能耗最低为优化目标,建立了以传感器测量值为输入、系统运行参数为输出的集中空调风-水系统优化控制模型;并提出随机增量粒子群算法(r PSO)用于求解优化控制模型,改进了传统粒子群算法在高维优化问题中的局限性。本文以上海市某公共建筑的大型集中空调为例,进行了优化控制建模和rPSO求解,仿真结果表明:相对于定送风温度的传统模式,优化控制的系统在中高负荷下可取得13.18%~13.45%的平均节能率;相较于传统粒子群算法,r PSO在高维优化求解中具有更好的性能和稳定性,对开发大型集中空调优化控制器具有参考价值。
Taking the minimum energy consumption as the optimization objective, an optimal control model of centralized air-conditioning system with sensor measurements as inputs and system operation parameters as outputs is established in this paper. The random incremental particle swarm optimization(rPSO) algorithm is proposed to solve the high-dimensional optimization problems corresponding to the optimal control model.Taking 64 districts of large centralized air-conditioning system in a public building in Shanghai as an example, the optimal control modeling and the rPSO solution are implemented. According to simulation results, some conclusions can be drawn as follows. Firstly, system energy saving rate is 13.27% and 18.52% respectively with optimal control, compared with 2 control groups with traditional fixed supply air temperature strategy. Secondly,compared with basic PSO, the rPSO algorithm has better performance and stability in solving high-dimensional optimization problems, which has reference value for the development of optimal controller of large-scale central air conditioning systems.
作者
王子轩
姚晔
赵鹏生
WANG Zixuan;YAO Ye;ZHAO Pengsheng(Institute of Refrigeration and Cryogenics,Shanghai Jiao Tong University,Shanghai,200240,China;Shanghai Genuis Building Technology Co.,Ltd.,Shanghai200331,China)
出处
《制冷技术》
2021年第4期20-26,共7页
Chinese Journal of Refrigeration Technology
关键词
空调系统节能
优化控制
高维度优化
随机增量粒子群算法
Air-conditioning system energy conservation
Optimal control
High dimensionality optimization
Random increment particle swarm optimization(rPSO)
