广州地铁建设管理有限公司;东南大学能源与环境学院;南京福加自动化科技有限公司;
空调系统功耗占建筑能耗的比重较大且存在节能空间,目前对空调系统的优化控制研究多侧重于局部设备控制,且控制并未考虑冷负荷的分配。本文以某地铁车站空调系统实测数据为基础,拟合该系统主要部件的功耗模型,并基于能量守恒和质量守恒得到全局功耗模型。验证模型预测精度后,利用花授粉算法,分析不同控制策略对系统节能的影响。结果表明,模型具有良好的预测精度,各设备功耗预测的R~2均大于0.95,MAPE均小于10%,一周内系统总功耗的预测值相对误差为6.81%;将冷负荷引入参数控制后,相比原控制策略和不引入冷负荷控制的控制策略,分别节能8.11%和4.08%。
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下载次数 | 被引频次 | 阅读次数 |
[1]丁翀.基于OPC-UA的地铁站空调智能控制系统及控制策略研究[D].杭州:杭州电子科技大学,2023.
[2] PAN S,WANG H W,PEI F,et al.An investigation on energy consumption of air conditioning system in Beijing subway stations[J].Energy Procedia,2017,142:2568-2573.
[3] BI H Q,ZHOU Y L,LIU J,et al.Load forecast and fuzzy control of the air-conditioning systems at the subway stations[J].Journal of Building Engineering,2022,49:104029.
[4] CHANG Y C,LIN J K,CHUANG M H.Optimal chiller loading by genetic algorithm for reducing energy consumption[J].Energy and Buildings,2005,37:147-155.
[5] CHANG Y C.Genetic algorithm based optimal chiller loading for energy conservation[J].Applied Thermal Engineering,2005,25:2800-2815.
[6] LEE W S,LIN L C.Optimal chiller loading by particle swarm algorithm for reducing energy consumption[J].Applied Thermal Engineering,2009,29:1730-1734.
[7] SOHRABI F,NAZARI-HERIS M,MOHAMMADIIVATLOO B,et al.Optimal chiller loading for saving energy by exchange market algorithm[J].Energy and Buildings,2018,169:245-253.
[8] QI M Y,LI J Q,HAN Y Y,et al.Optimal chiller loading for energy conservation using an improved fruit fly optimization algorithm[J].Energies,2020,13(15):3760.
[9]丁伟翔,袁建红,杨英,等.基于遗传算法的并联冷水机组负荷优化分配策略[J].制冷与空调,2022,22(4):17-20.
[10] HUSSIAN S A,WANB H,KASUNA H,et al.Distributed real-time optimal control of central air-conditioning systems[J].Energy and Buildings,2017,142:2568-2573.
[11]刘真全.基于人工鱼群算法的空调水系统优化控制研究[D].沈阳:沈阳建筑大学,2014.
[12]喻锴,张九根,朱元.中央空调冷冻水系统遗传蚁群算法优化控制研究[J].现代电子技术,2019,42(11):135-139.
[13]赵靖,杜亚慧.基于遗传算法的中央空调水系统动态调控研究[J].建筑热能通风空调,2021,40(10):1-6,49.
[14]于军琪,高之坤,赵安军,等.中央空调冷冻水系统设备节能优化方法[J].哈尔滨工业大学学报,2022,54(12):143-150.
[15] LING J H,DAI N,XING J C,et al.An improved input variable selection method of the data-driven model for building heating load prediction[J].Journal of Building Engineering,2021,44:103255.
[16] YANG S Y,YU J Q,GAO Z K,et al.Energy-saving optimization of air-conditioning water system based on data-driven and improved parallel artificial immune system algorithm[J].Energy Conversion and Management,2023,283:116902.
[17] YANG X S,KARAMANOGLU M,HE X.Flower pollination algorithm:a novel approach for multi objective optimization[J].Engineering Optimization,2014,46:1222-1237.
基本信息:
DOI:10.20245/j.issn.1009-8402.2025.03.003
中图分类号:TP18;TB657.2
引用信息:
[1]冯泽,胡远洋,杨兴舟等.基于花授粉算法的空调系统节能控制[J].制冷与空调,2025,25(03):16-21+26.DOI:10.20245/j.issn.1009-8402.2025.03.003.
基金信息: