变频空调实际运行模式识别及数据挖掘

doi:10.3901/JME.2019.06.194

机械工程学报 ›› 2019, Vol. 55 ›› Issue (6): 194-202.doi: 10.3901/JME.2019.06.194

• 可再生能源与工程热物理 • 上一篇下一篇

变频空调实际运行模式识别及数据挖掘

梁志豪¹, 巫江虹¹, 谢子立²

1. 华南理工大学机械与汽车工程学院广州 510641;
2. 多伦多大学文理学院安大略 M5S 2E8 加拿大

收稿日期:2018-03-06 修回日期:2018-10-28 出版日期:2019-03-20 发布日期:2019-03-20
通讯作者: 巫江虹(通信作者),女,1967年出生,博士,教授,博士研究生导师。主要研究方向为相变蓄热热泵技术、室温磁制冷、移动空调。E-mail:pmjhwu@scut.edu.cn
作者简介:梁志豪,男,1990年出生。主要研究方向为基于数据挖掘的房间空调器长效性能。E-mail:13480204681@139.com;谢子立,男,1995年出生。主要研究方向为大数据挖掘。Email:zili.xie@mail.utoronto.ca
基金资助:
国家自然科学基金（51776076）和国家重点研发计划“政府间国际科技创新合作”重点专项（2016YFE011810）资助项目。

Variable Frequency Room Air Conditioner Operation Pattern Recognition and Data Mining

LIANG Zhihao¹, WU Jianghong¹, XIE Zili²

1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641;
2. College of Arts and Sciences, University of Toronto, Ontario, M5S 2E8 Canada

Received:2018-03-06 Revised:2018-10-28 Online:2019-03-20 Published:2019-03-20

摘要/Abstract

摘要： 变频空调的长期运行数据是空调在实际环境中运行状态最直接的反映，空调运行的内在规律潜藏在其长期的运行数据中，分析其长期运行数据时，由于数据冗余、存在异常数据等原因，需要采用数据挖掘的方法进行研究。采用聚类算法进行分析，空调的运行模式可总结为三种模式，即上午高负荷模式、下午高负荷模式以及低频平稳模式，三种模式下空调的送风温度都比较接近，但室外温度有所区别。上午（下午）高负荷模式中，室外温度在上午（下午）最高，功率及制冷性能系数（Energy efficiency ratio，EER）分别处于峰值及谷值，低频平稳模式下，室外温度并不高，开机后空调运行比较稳定，运行参数变化不大。此后，采用控制变量法和多项式拟合来分析环境工况参数对空调性能的影响，室外侧环境温度、空调回风温度的升高均导致空调的功率增加，而空调的EER随着回风温度的升高而增加，随着室外温度及空调的功率（运行频率）的增加而下降。采用神经网络算法，研究基于空调送风侧参数预测变频空调性能的方法，并建立神经网络预测模型，该方法的预测误差在15%以内，可以通过控制空调运行时的送风侧参数，达到控制空调性能的目的。最后，根据以上研究成果，提出变频空调控制优化方法，提高空调在线长期运行能效。

关键词: 变频空调, 模式识别, 能耗分析, 数据挖掘, 优化运行

Abstract: The long running data of the variable frequency room air conditioners is the most direct reflection of their operation state in actual surroundings. Since the inherent law of air conditioners is hidden behind the long term operation data with a great quantity of redundancy and outlier data, data mining method is needed to solve this problem. The clustering algorithm is used for the analysis of air conditioners' operation modes. Three modes are concluded:morning high load mode, afternoon high load mode and stable low frequency mode. The temperature of air supply in the three modes is relatively similar, but the outdoor temperature is different. In the morning high load or afternoon high load mode, the outdoor temperature peaks in the morning and afternoon, meanwhile the energy consumption reaches the maximum and the energy efficiency ratio (EER) reaches the minimum respectively. In the stable low frequency mode, the outdoor temperature is not high, the operation is relatively stable after starting, and the operating parameters are relatively stable. Since then, the controlling variables method and polynomial fitting method are applied to analyze the relationships between the operation parameters and the performance of the air conditioners. It shows that air conditioner energy consumption will increase with the increase of outdoor temperature, and the EER will rise as the indoor temperature increases but will decrease as the energy consumption (frequency) increases. Then the neural network algorithm is used to predict the performance of inverter air conditioner based on the air side parameters. And this model has been proved to be effective in predicting air conditioner energy consumption and EER with the error range within 15%, which means it can be used to control air conditioner performance through air conditioner inlet air parameters. Finally, according to the above research results, the optimization method of variable frequency air conditioner control is put forward to improve air conditioners' long term operation energy efficiency.

Key words: data mining, operation optimization, pattern recognition, performance analysis, variable frequency air conditioner

中图分类号:

TK121

梁志豪, 巫江虹, 谢子立. 变频空调实际运行模式识别及数据挖掘[J]. 机械工程学报, 2019, 55(6): 194-202.

LIANG Zhihao, WU Jianghong, XIE Zili. Variable Frequency Room Air Conditioner Operation Pattern Recognition and Data Mining[J]. Journal of Mechanical Engineering, 2019, 55(6): 194-202.

参考文献

[1] 王起霄,刘淑静,汝长海. 变频空调的性能研究[J]. 哈尔滨商业大学学报,2001,17(3):69-71. WANG Qixiao,LIU Shujing,RU Changhai. Discussion on capability of frequency conversion air conditioner[J]. Journal of Harbin University of Commerce,2001,17(3):69-71.
[2] 周兴禧,周滋锋,王懿,等. 变频空调系统特性的仿真研究[J]. 流体机械,2000,28(2):43-47. ZHOU Xingxi,ZHOU Zifeng,WANG Yi,et al. Simulation research on characters of air-conditioner with inverter[J]. Fluid Machinery,2000,28(2):43-47.
[3] 张宏亮,刘金平. 家用变频空调冷负荷分析与节能探讨[J]. 暖通空调,2003,33(6):119-122. ZHANG Hongliang,LIU Jinping. Cooling load analysis and energy saving study for domestic variable-frequency air conditioners[J]. Heating Ventilating & Air Conditioning,2003,33(6):119-122.
[4] 姚兵. 定频空调和交流变频空调能效对比分析[J]. 家电科技,2004(8):70-73. YAO Bing. Energy efficiency comparison of constant speed and variable frequency air conditioners[J]. Science and Technology of Household Electric Appliance,2004(8):70-73.
[5] 黄辉,马颖江,张有林,等. 减小变频空调单转子压缩机低频转速波动的方法[J]. 电机与控制学报,2011,15(3):98-102. HUANG Hui,MA Yingjiang,ZHANG Youlin. Method to reduce the speed ripples of single rotator compressor of air conditioner in low frequency[J]. Electric Machines and Control,2011,15(3):98-102.
[6] 陈宁,张跃,桂卫华,等. 变频空调驱动电机的弱磁控制[J]. 电机与控制学报,2014,18(7):65-71. CHEN Ning,ZHANG Yue,GUI Weihua. Flux weakening control for drive motor of inverter air conditioner[J]. Electric Machines and Control,2014,18(7):65-71.
[7] 殷光文. 定速及变频空调器节能技木的探讨[J]. 暖通空调,2009,39(1):88-91. YIN Guangwen. Energy saving discussion for constant speed and variable frequency air conditioners[J]. Heating Ventilating & Air Conditioning,2009,39(1):88-91.
[8] 刘永彬. 运行环境对变频空调器转速及能耗影响的理论与试验研究[D]. 北京:北京工业大学,2012. LIU Yongbin. Theoretical and experimental research of the operating environment on the rotational speed and energy consumption of the inverter air-conditioner[D]. Beijing:Beijing University of Technology,2012.
[9] 张微. 外界参数与使用习惯对空调器运行效率和能耗影响的仿真研究[D]. 北京:北京工业大学,2013. ZHANG Wei. Simulation researches on influences of external parameters and user's habits on operation efficiency and energy consumption of air conditioners[D]. Beijing:Beijing University of Technology,2013.
[10] 晋欣桥,杜志敏,惠广海,等. 楼宇空调水系统温度传感器固定偏差故障的诊断方法[J]. 机械工程学报,2004,40(9):42-47. JIN Xinqiao,DU Zhimin,HUI Guanghai,et al. Fault detection and diagnosis method for the temperature sensors with fixed bias in water systems of HVAC[J]. Journal of Mechanical Engineering,2004,40(9):42-47.
[11] 王萌,孙树栋. 基于优化核空间的制造过程质量分析算法[J]. 机械工程学报,2012,48(22):182-188. WANG Meng,SUN Shudong,Optimized kernel space based algorithm for quality data analysis[J]. Journal of Mechanical Engineering,2012,48(22):182-188.
[12] YANG G,TUMWESIGYE E,CAHILL B,et al. Using data mining in optimisation of building energy consumption and thermal comfort management[C]//Proceedings of the 2nd International Conference on Software Engineering and Data Mining. Chengdu:IEEE,2010:434-439.
[13] 李炎波,吴凌燕. 数据时代的室内舒适环境控制方法[J]. 建筑热能通风空调,2017,36(11):41-43. LI Yanbo,WU Lingyan. Control method of comfort able indoor environment in DT age[J]. Building Energy & Environment,2017,36(11):41-43.
[14] 雷亚国,贾峰,周昕,等. 基于深度学习理论的机械备大数据健康监测方法[J]. 机械工程学报,2015,51(21):49-56. LEI Yaguo,JIA Feng,ZHOU Xin,et al. A deep learning-based method for machinery health monitoring with big data[J]. Journal of Mechanical Engineering,2015,51(21):49-56.
[15] HOU Z,LIAN Z,YAO Y,et al. Data mining based sensor fault diagnosis and validation for building air conditioning system[J]. Energy Conversion and Management,2006,47(15-16):2479-2490.
[16] YU Z,HAGHIGHAT F,FUNG B C M,et al. A novel methodology for knowledge discovery through mining associations between building operational data[J]. Energy and Buildings,2012,47:430-440.
[17] 杨文献,姜节胜. 基于数据挖掘的柴油机气门故障诊断技术研究[J]. 机械工程学报,2004,40(10):25-29. YANG Wenxian,JIANG Jiesheng,Data mining based technique for diagnosing the faults presenting on engine valves[J]. Chinese Journal of Mechanical Engineering,2004,40(10):25-29.
[18] 巫江虹,刘超鹏,梁志豪,等. 房间空调器长效运行性能预测及优化方案的研究[J]. 机械工程学报,2015,51(18):158-166. WU Jianghong,LIU Chaopeng,LIANG Zhihao,et al. Research on the room air conditioner long-term performance prediction and optimization strategy[J]. Journal of Mechanical Engineering,2015,51(18):158-166.

变频空调实际运行模式识别及数据挖掘

Variable Frequency Room Air Conditioner Operation Pattern Recognition and Data Mining

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