多联机空调系统在线能耗测量方法研究
摘要：实时运行能耗是实现多联机节能运行和智能控制的基础数据，但因成本原因，实际运行的多联机空调系统很少安装功率表对其能耗进行实时监测，因此研发低成本且具有工程精度的实时能耗测量方法对于多联机的节能降碳具有重要意义。本文提出了适用于单片机运行的频率-电流曲面拟合法和适用于云端大数据运算的神经网络自学习法，实验结果表明，2种在线能耗测量算法均具有良好的测量精度，在名义制冷、名义制热、最大制冷、低温制热和除霜等工况及在各种工况下自由切换连续45 h运行时，对多联机系统总功率的测量误差均在±5%以内。
关键词：多联机能耗在线测量数据拟合神经网络
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参考文献[1] 清华大学建筑节能研究中心.中国建筑节能年度发展研究报告[M].北京：中国建筑工业出版社，2020:12- 13.
[2] 机电信息编辑部.2020年中国中央空调行业市场总结报告[J].机电信息，2021(4):17- 19.
[3] XIAO H S,SHI J F,YANG Z X,et al.Precision improvement method for onsite performance measurement of variable refrigerant flow system[J].Building and environment,2022,208:108626.
[4] 杨怀毅，任韬，丁国良，等.基于有限测点的空调系统性能在线监测方法[J].制冷学报，2018,39(6):70- 76.
[5] YU Z,HAGHIGHAT F,FUNG B C M.Advances and challenges in building engineering and data mining applications for energy-efficient communities[J].Sustainable cities and society,2016,25:33- 38.
[6] DEB C,LEE S E,SANTAMOURIS M.Using artificial neural networks to assess HVAC related energy saving in retrofitted office buildings[J].Solar energy,2018,163:32- 44.
[7] PAUDEL S,ELMITRI M,COUTURIER S,et al.A relevant data selection method for energy consumption prediction of low energy building based on support vector machine[J].Energy and buildings,2017,138:240- 256.
[8] ZHAO D Y,ZHONG M,ZHANG X,et al.Energy consumption predicting model of VRV (variable refrigerant volume) system in office buildings based on data mining[J].Energy,2016,102:660- 668.
[9] DENG H,FANNON D,ECKELMAN M J.Predictive modeling for US commercial building energy use:a comparison of existing statistical and machine learning algorithms using CBECS microdata[J].Energy and buildings,2018,163:34- 43.
[10] 张春路.制冷空调系统仿真原理与技术[M].北京：化学工业出版社，2013:3- 6.
[11] 周宴平.变频多联空调系统的能耗分析和实验研究[D].上海：上海交通大学，2008:48- 50.
[12] 唐小谦，陈焕新，郭亚宾.基于数据挖掘的多联机能耗预测[J].制冷技术，2020,40(3):8- 12,23.
[13] GUO Y B,LI G N,CHEN H X,et al.Development of a virtual variable-speed compressor power sensor for variable refrigerant flow air conditioning system[J].International journal of refrigeration,2017,74:73- 85.
[14] PARK B R,CHOI E J,HONG J,et al.Development of an energy cost prediction model for a VRF heating system[J].Applied thermal engineering,2018,140:476- 486.
[15] CHUNG M H,YANG Y K,LEE K H,et al.Application of artificial neural networks for determining energy efficient operating set-points of the VRF cooling system [J].Building and environment,2017,125:77- 87.
[16] 苏玉海，陈宗衍，王槃，等.一种多联机能耗计量方法及系统：CN111473471A[P].2020-07-31.
[17] 巫奕秋，牟桂贤，陈宗衍，等.能耗计量方法、装置及电器：CN111830350A[P].2020-10-27.
[2] 机电信息编辑部.2020年中国中央空调行业市场总结报告[J].机电信息，2021(4):17- 19.
[3] XIAO H S,SHI J F,YANG Z X,et al.Precision improvement method for onsite performance measurement of variable refrigerant flow system[J].Building and environment,2022,208:108626.
[4] 杨怀毅，任韬，丁国良，等.基于有限测点的空调系统性能在线监测方法[J].制冷学报，2018,39(6):70- 76.
[5] YU Z,HAGHIGHAT F,FUNG B C M.Advances and challenges in building engineering and data mining applications for energy-efficient communities[J].Sustainable cities and society,2016,25:33- 38.
[6] DEB C,LEE S E,SANTAMOURIS M.Using artificial neural networks to assess HVAC related energy saving in retrofitted office buildings[J].Solar energy,2018,163:32- 44.
[7] PAUDEL S,ELMITRI M,COUTURIER S,et al.A relevant data selection method for energy consumption prediction of low energy building based on support vector machine[J].Energy and buildings,2017,138:240- 256.
[8] ZHAO D Y,ZHONG M,ZHANG X,et al.Energy consumption predicting model of VRV (variable refrigerant volume) system in office buildings based on data mining[J].Energy,2016,102:660- 668.
[9] DENG H,FANNON D,ECKELMAN M J.Predictive modeling for US commercial building energy use:a comparison of existing statistical and machine learning algorithms using CBECS microdata[J].Energy and buildings,2018,163:34- 43.
[10] 张春路.制冷空调系统仿真原理与技术[M].北京：化学工业出版社，2013:3- 6.
[11] 周宴平.变频多联空调系统的能耗分析和实验研究[D].上海：上海交通大学，2008:48- 50.
[12] 唐小谦，陈焕新，郭亚宾.基于数据挖掘的多联机能耗预测[J].制冷技术，2020,40(3):8- 12,23.
[13] GUO Y B,LI G N,CHEN H X,et al.Development of a virtual variable-speed compressor power sensor for variable refrigerant flow air conditioning system[J].International journal of refrigeration,2017,74:73- 85.
[14] PARK B R,CHOI E J,HONG J,et al.Development of an energy cost prediction model for a VRF heating system[J].Applied thermal engineering,2018,140:476- 486.
[15] CHUNG M H,YANG Y K,LEE K H,et al.Application of artificial neural networks for determining energy efficient operating set-points of the VRF cooling system [J].Building and environment,2017,125:77- 87.
[16] 苏玉海，陈宗衍，王槃，等.一种多联机能耗计量方法及系统：CN111473471A[P].2020-07-31.
[17] 巫奕秋，牟桂贤，陈宗衍，等.能耗计量方法、装置及电器：CN111830350A[P].2020-10-27.
Research on online energy consumption metering methods for variable refrigerant flow air conditioning systems
Abstract: Real-time operation energy consumption is the basic data of the energy-saving operation and online smart control for variable refrigerant flow(VRF) systems. However, power meter is rarely installed on VRF systems for energy consumption monitoring in real projects due to their high cost. As a result, research on economy and accurate real-time energy consumption measuring method for VRF systems is of great significance for energy saving and carbon reduction. In this paper, a frequency-current surface fitting method in single chip microcomputer cases and a neural network self-learning method in cloud big data cases are proposed. Experimental results illustrate that both two methods show high measurement accuracy. The measurement errors of the total power of the VRF system distribute within ±5% in various operating conditions including nominal cooling, nominal heating, maximum cooling, low temperature heating, defrosting and 45 h free switching operating conditions.
Keywords: variable refrigerant flow unit; energy consumption; online metering; data fitting; neural network;
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