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ABSTRACT
Long-term performance of room air conditioners (RAC) is an important evaluation index to estimate RAC actual energy conservation efficiency. The environmental condition change and long-term use will result in degradation of RAC performance, which increases energy consumption. To solve the problem, an optimization method was proposed. The operating data was collected by a RAC online monitoring system. The hidden relationship among operating parameters was discovered by data mining method, and then an artificial neural network prediction model was developed. After training, this model had been validated to be effective in predicting energy efficiency ratio (EER) with absolute fraction of variance of 0.990 and root mean square error of 0.291. Moreover, the prediction results of return air temperature, relative humidity and EER agreed well with measured values. Based on the prediction results and historical operating database, an optimal control strategy was proposed to improve the operating performance of RAC. By applying the control strategy the energy saving rate was theoretically about 9% in the stable operation case and 2.61% in all actual operating period.
Acknowledgments
In the research work, we got help from Guangzhou Vkan Certification and Testing Institute (CVC), from China Quality Certification Center (CQC), from domestic air conditioners manufactures such as Gree, Midea, Chigo, AUX and ChangHong. We really appreciate their help and would like express sincerely thanks to them. We especially want to thank chief engineer Wu Zhidong, senior engineer Yuan Yaqing, and other engineers of CVC and CQC. The authors gratefully acknowledge the General Administration of Quality Supervision, Inspection and Quarantine and Foreign Economic Cooperation Office, Ministry of Environmental Protection of the People's Republic of China, which offered financial support by the project C/III/S/15/008 and C/III/S/15/398. The work presented in this paper was also sponsored by the National Natural Science Foundation of China (No. 51776076).
Nomenclature
| ANN | = | artificial neural network |
| A | = | area of air outlet (m2) |
| Ae | = | effective heat transfer area of room enclosure (m2) |
| d | = | moisture content (g/kg) |
| EER | = | energy efficiency ratio |
| FP-Growth | = | frequent pattern growth algorithm |
| GPRS | = | general packet radio service |
| GSM | = | global system for mobile communications |
| HVAC | = | heating, ventilation and air conditioning |
| h | = | enthalpy (kJ/kg) |
| Δh | = | enthalpy difference (kJ/kg) |
| hr | = | return air enthalpy (kJ/kg) |
| hs | = | supply air enthalpy (kJ/kg) |
| K | = | overall heat transfer coefficient of building enclosure (W/(m2K)) |
| n | = | number of data patterns |
| P | = | input power (W) |
| Pb | = | atmosphere pressure (kPa) |
| Ps | = | saturated vapor pressure (kPa) |
| Qc | = | cooling capacity (W) |
| Qg | = | the heat generated inside the room (kJ) |
| qm | = | mass flow rate (kg/s) |
| R2 | = | absolute fraction of variance |
| RAC | = | room air conditioners |
| Rg | = | gas constant of wet air (J/(kgK)) |
| RH | = | relative humidity |
| RMSE | = | root mean square error |
| SEER | = | seasonal energy efficiency ratio |
| ta | = | air temperature (°C) |
| Ta | = | air absolute temperature (K) |
| tmea,m | = | measured values |
| u | = | mean supply air speed (m/s) |
| ypre,m | = | predicted values |
Greek symbols
| αi | = | current measured value of parameters |
| αi,j | = | measured value of previous 10 minutes |
| βi | = | tolerance limit |
| ϕ | = | relative humidity |
| ρ | = | air density (kg/m3) |
Subscripts
| a | = | dry air |
| i | = | current number |
| in | = | indoor |
| j | = | minute before current number |
| limit | = | limit value |
| m | = | data point |
| mea | = | measured |
| out | = | outdoor |
| pre | = | predicted |
| v | = | water vapor |
Additional information
Notes on contributors
Jianghong Wu
Jianghong Wu is a professor in the School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, Guangdong, China. She was granted a master's degree in 1990 and a Ph.D. degree in 1996 in refrigeration and cryogenics engineering from Xi'an Jiaotong University. She worked on energy conservation of air-conditioning system, room temperature magnetic refrigeration and new energy heat pump system.
Biwang Lu
Biwang Lu is a Ph.D. student in the School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, Guangdong, China. He has conducted research on long-term performance and energy conservation of room air conditioners and room temperature magnetic refrigeration for more than 2 years. He has worked under the advisory of Prof. Wu at South China University of Technology since 2014.
Zhihao Liang
Zhihao Liang is a master student graduated from the School of Mechanical and Automotive En-gineering, South China Univer-sity of Technology, Guangzhou, Guangdong, China. He has conducted research on long-term performance, control strategy and energy conservation of room air conditioners for more than 3 years. His master degree's research focused on the long-term performance study of room air conditioners based on data mining. Now he is working in Danfoss Automatic Controls management (Shanghai) Co., Ltd.