基于外部参照优化算法的空调系统节能控制
|
摘要
分析了当前空调系统能耗优化算法存在的不足,根据空调系统非线性与时滞性的特点,提出了采用多目标粒子群优化算法进行寻优计算。将空调系统分为3个目标解,寻取多目标最优解Pareto前沿,根据Pareto前沿求得空调系统整体能耗最优值。普通多目标粒子群算法收敛性相对较差、仿真时间较长,提出了外部参照的多目标粒子群改进算法来提高收敛性与快速性。通过仿真实验和能耗分析对比发现,相较于其他算法,该改进算法的优化节能效果更佳。
Analyses the shortcomings of the current energy consumption optimization algorithm for air conditioning system, and proposes a multi-objective particle swarm optimization(PSO) algorithm according to the characteristics of non-linearity and time delay of air conditioning system. Divides the air conditioning system into three target solutions, and finds the multi-objective optimal Pareto frontiers. According to Pareto frontiers, obtains the optimal value of overall energy consumption of air conditioning system. Because the convergence of the ordinary multi-objective PSO algorithm is relatively poor and the simulation time is too long, proposes an external reference multi-objective PSO algorithm to improve the convergence and rapidity. The simulation experiment and energy consumption analysis show that compared with other algorithms, the optimization and energy saving effect of the improved algorithm is better.
Analyses the shortcomings of the current energy consumption optimization algorithm for air conditioning system, and proposes a multi-objective particle swarm optimization(PSO) algorithm according to the characteristics of non-linearity and time delay of air conditioning system. Divides the air conditioning system into three target solutions, and finds the multi-objective optimal Pareto frontiers. According to Pareto frontiers, obtains the optimal value of overall energy consumption of air conditioning system. Because the convergence of the ordinary multi-objective PSO algorithm is relatively poor and the simulation time is too long, proposes an external reference multi-objective PSO algorithm to improve the convergence and rapidity. The simulation experiment and energy consumption analysis show that compared with other algorithms, the optimization and energy saving effect of the improved algorithm is better.
引文
[1] 刘兆辉,李震宇,谭洪卫,等.办公楼变频空气源热泵序列优化控制[J].制冷学报,2017,38(5):21- 28
[2] 蒋红梅,李战明,唐伟强,等.变风量空调系统的优化控制研究[J].暖通空调,2016,46(3):84- 88
[3] 闫军威,陈城,周璇,等.多台冷水机组负荷分配优化策略仿真研究[J].暖通空调,2016,46(4):98- 104
[4] CAI J,KIM D,PUTTA V K,et al.Multi-agent control for centralized air conditioning systems serving multi-zone buildings[C]// American Control Conference IEEE,2015:986- 993
[5] 陈大鹏,张九根,梁星.基于免疫粒子群算法的中央空调冷冻水系统优化控制[J].计算机应用,2017,37(9):2717- 2721
[6] 董崇杰,刘毅,彭勇.改进布谷鸟算法在人群疏散多目标优化中的应用[J].系统仿真学报,2016,28(5):1063- 1069
[7] 于凤存,方国华,王文杰,等.基于多目标遗传算法的南水北调东线工程湖泊群优化调度研究[J].灌溉排水学报,2016,35(3):78- 85
[8] 张雅,任庆昌,向虎,等.变风量空调机组送风温度系统建模与控制研究[J].暖通空调,2007,37(12):125- 128
[9] 李明海.中央空调水系统的优化控制与节能技术研究[D].西安:西安建筑科技大学,2011:16- 17
[10] KUSIAK A,LI M Y,TANG F.Modeling and optimization of HVAC energy consumption[J].Applied Energy,2010,87(10):3092- 3102
[11] WANG J G,XIAO Q P,WANG J J,et al.Soft sensor approach for optimal operation of cooling tower for energy conservation[C]//Control and Decision Conference IEEE,2014:3612- 3615
[12] LU L,CAI W J,CHAI Y S,et al.Global optimization for overall HVAC systems—part I problem formulation and analysis[J].Energy Conversion and Management,2005,46(7/8):999- 1014
[2] 蒋红梅,李战明,唐伟强,等.变风量空调系统的优化控制研究[J].暖通空调,2016,46(3):84- 88
[3] 闫军威,陈城,周璇,等.多台冷水机组负荷分配优化策略仿真研究[J].暖通空调,2016,46(4):98- 104
[4] CAI J,KIM D,PUTTA V K,et al.Multi-agent control for centralized air conditioning systems serving multi-zone buildings[C]// American Control Conference IEEE,2015:986- 993
[5] 陈大鹏,张九根,梁星.基于免疫粒子群算法的中央空调冷冻水系统优化控制[J].计算机应用,2017,37(9):2717- 2721
[6] 董崇杰,刘毅,彭勇.改进布谷鸟算法在人群疏散多目标优化中的应用[J].系统仿真学报,2016,28(5):1063- 1069
[7] 于凤存,方国华,王文杰,等.基于多目标遗传算法的南水北调东线工程湖泊群优化调度研究[J].灌溉排水学报,2016,35(3):78- 85
[8] 张雅,任庆昌,向虎,等.变风量空调机组送风温度系统建模与控制研究[J].暖通空调,2007,37(12):125- 128
[9] 李明海.中央空调水系统的优化控制与节能技术研究[D].西安:西安建筑科技大学,2011:16- 17
[10] KUSIAK A,LI M Y,TANG F.Modeling and optimization of HVAC energy consumption[J].Applied Energy,2010,87(10):3092- 3102
[11] WANG J G,XIAO Q P,WANG J J,et al.Soft sensor approach for optimal operation of cooling tower for energy conservation[C]//Control and Decision Conference IEEE,2014:3612- 3615
[12] LU L,CAI W J,CHAI Y S,et al.Global optimization for overall HVAC systems—part I problem formulation and analysis[J].Energy Conversion and Management,2005,46(7/8):999- 1014