基于负荷预测的中央空调制冷系统能耗综合优化
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摘要
针对中央空调制冷系统能耗过高的情况,研究了其能耗优化问题。利用支持向量机,分别建立了中央空调负荷预测模型及制冷系统中各设备的能耗模型。在此基础上,建立了制冷系统能耗综合优化方程组,并提出了一种改进的粒子群优化算法对制冷系统能耗优化方程组进行优化求解,获得优化生成方案。研究结果表明,所提方法有效的降低了制冷系统运行过程的电能消耗,减少了用电费用。
Aiming at reducing the energy consumption of HAVC system, an optimal control strategy based on load prediction was analyzed. The load forecasting model of central air conditioning was established by using support vector machine. Next, an improved particle swam algorithm was produced to optimize the operation parameters of HVAC under variations of cooling load. The simulation result show that the proposed strategy could reduce the energy consumption effectively.
Aiming at reducing the energy consumption of HAVC system, an optimal control strategy based on load prediction was analyzed. The load forecasting model of central air conditioning was established by using support vector machine. Next, an improved particle swam algorithm was produced to optimize the operation parameters of HVAC under variations of cooling load. The simulation result show that the proposed strategy could reduce the energy consumption effectively.
引文
[1]王日英,罗文广,吴小娜.中央空调冷却水系统节能优化控制研究[J].计算机测量与控制, 2014, 22(11):3568-3571.
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[8]蒋小强,龙惟定.集中空调变流量协同控制策略[J].暖通空调,2013, 3(6):116-119.
[9]林晓伟,王侠.地铁通风空调系统的优化控制[J].城市轨道交通研究, 2012, 15(11):100-104.
[10] Zhang B, Yang C, Zhu H, et al. Kinetic Modeling and Parameter Estimation for Competing Reactions in Copper Removal Process from Zinc Sulfate Solution[J]. Industrial&Engineering Chemistry Research, 2013, 52(48):17074-17086.
[11]张爱伟,李金新.一种改进的粒子群优化算法[J].杭州电子科技大学学报(自然科学版), 2016, 36(6):10-14.
[12]邢建厂,李勇,孟庆喜.基于改进简化粒子群算法的转炉微差压优化控制[J].工业控制计算机, 2018, 31(12):8-10.
[13]王磊,王行甫,苗付友.一种带有二维扰动和自适应学习因子的粒子群算法[J].小型微型计算机系统, 2018, 39(11):2353-2357.
[2] Sen Huang, Wangda Zuo, Michael D. Sohn. Improved cooling tower control of legacy chiller plants by optimizing the condenser water set poin[J]. Building and Environment, 2017, 111:33-46.
[3] Alessandro Beghi, Luca Cecchinato, Mirco Rampazzo. A multiphase genetic algorithm for the efficient management of multichiller systems[J]. Energy Conversion and Management, 2011, 52(3):1650-1661.
[4] Majid Karami, Liping Wang. Particle Swarm optimization for control operation of an all-variable speed water-cooled chiller plant[J]. Applied Thermal Engineering, 2018, 130:962-978.
[5]刘庆典,周璇,凡祖兵.基于混沌支持向量回归机的短期空调负荷预测[J].建筑科学, 2016, 32(6):102-107.
[6]贺红林,周翔,曾劲松.短期负荷预测的简化LS-SVM模型及实现[J].计算机仿真, 2011, 28(1):302-306.
[7]王亮,卢军.空调系统冷却塔优化运行方式研究[J].暖通空调,2011, 41(7):141-144.
[8]蒋小强,龙惟定.集中空调变流量协同控制策略[J].暖通空调,2013, 3(6):116-119.
[9]林晓伟,王侠.地铁通风空调系统的优化控制[J].城市轨道交通研究, 2012, 15(11):100-104.
[10] Zhang B, Yang C, Zhu H, et al. Kinetic Modeling and Parameter Estimation for Competing Reactions in Copper Removal Process from Zinc Sulfate Solution[J]. Industrial&Engineering Chemistry Research, 2013, 52(48):17074-17086.
[11]张爱伟,李金新.一种改进的粒子群优化算法[J].杭州电子科技大学学报(自然科学版), 2016, 36(6):10-14.
[12]邢建厂,李勇,孟庆喜.基于改进简化粒子群算法的转炉微差压优化控制[J].工业控制计算机, 2018, 31(12):8-10.
[13]王磊,王行甫,苗付友.一种带有二维扰动和自适应学习因子的粒子群算法[J].小型微型计算机系统, 2018, 39(11):2353-2357.