TY - JOUR
T1 - Indirect evaporative cooling maps of China
T2 - Optimal and quick performance identification based on a data-driven model
AU - Shi, Wenchao
AU - Ma, Xiaochen
AU - Gu, Yu
AU - Min, Yunran
AU - Yang, Hongxing
N1 - Funding Information:
The authors wish to acknowledge the financial support provided by the General Research Fund projects of the Hong Kong Research Grant Council (Ref. No.: 15213219 and 15200420). Our appreciation also goes to the Electrical and Mechanical Services Department of the Hong Kong SAR Government for supporting the on-site tests of an IEC system.
Funding Information:
The authors wish to acknowledge the financial support provided by the General Research Fund projects of the Hong Kong Research Grant Council (Ref. No.: 15213219 and 15200420). Our appreciation also goes to the Electrical and Mechanical Services Department of the Hong Kong SAR Government for supporting the on-site tests of an IEC system.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/9/15
Y1 - 2022/9/15
N2 - The data-driven models of various air conditioning (AC) systems have been developed because of the wider application of machine learning in the engineering field. Indirect evaporative cooler (IEC), known as one of the effective and environment-friendly AC devices, achieves the cooling purpose without using any types of mechanical compressors or chemical refrigerants. Recent studies on various IECs have been carried out in full swing with a large amount of valuable data produced. However, the data-driven model of the cross-flow IEC for sensible and total cooling is yet to be developed. In addition, by extracting the indoor cold exhaust air into the secondary air channel, the application range of an IEC can be extended, but so far the performance of IEC used in different regions has been rarely evaluated. In this study, an IEC model was established based on the artificial neural network (ANN), which was validated with on-site measurement results from a real engineering project. Combining the selected geometric size of IEC and various outdoor weather conditions into the IEC-ANN model, a case study was conducted to present the annual and seasonal IEC performance maps of China, and the optimal application regions could be determined. Results show that south China, east China, and middle China are more suitable to employ IEC for air treatment and energy saving. In south China, the greatest average temperature drop caused by the IEC is 4.52 ℃. The maximum cooling capacity can reach 5.74 kW, and it accounts for 30.1 % of the total cooling load. In the typical office building, the seasonal energy saving of the IEC with the given size is up to 3.64 kWh/m2, and the annual energy saving can reach 6.02 kWh/m2. In addition, the inference time of this IEC-ANN model was significantly shorter compared with a numerical model. Based on the quick prediction speed, the model can improve the working efficiency in the design stage of the engineering and may provide a swift response to guide the system operation.
AB - The data-driven models of various air conditioning (AC) systems have been developed because of the wider application of machine learning in the engineering field. Indirect evaporative cooler (IEC), known as one of the effective and environment-friendly AC devices, achieves the cooling purpose without using any types of mechanical compressors or chemical refrigerants. Recent studies on various IECs have been carried out in full swing with a large amount of valuable data produced. However, the data-driven model of the cross-flow IEC for sensible and total cooling is yet to be developed. In addition, by extracting the indoor cold exhaust air into the secondary air channel, the application range of an IEC can be extended, but so far the performance of IEC used in different regions has been rarely evaluated. In this study, an IEC model was established based on the artificial neural network (ANN), which was validated with on-site measurement results from a real engineering project. Combining the selected geometric size of IEC and various outdoor weather conditions into the IEC-ANN model, a case study was conducted to present the annual and seasonal IEC performance maps of China, and the optimal application regions could be determined. Results show that south China, east China, and middle China are more suitable to employ IEC for air treatment and energy saving. In south China, the greatest average temperature drop caused by the IEC is 4.52 ℃. The maximum cooling capacity can reach 5.74 kW, and it accounts for 30.1 % of the total cooling load. In the typical office building, the seasonal energy saving of the IEC with the given size is up to 3.64 kWh/m2, and the annual energy saving can reach 6.02 kWh/m2. In addition, the inference time of this IEC-ANN model was significantly shorter compared with a numerical model. Based on the quick prediction speed, the model can improve the working efficiency in the design stage of the engineering and may provide a swift response to guide the system operation.
KW - Air conditioning
KW - Cooling map
KW - Data-driven model
KW - Indirect evaporative cooling
KW - Optimal and quick identification
UR - http://www.scopus.com/inward/record.url?scp=85135501193&partnerID=8YFLogxK
U2 - 10.1016/j.enconman.2022.116047
DO - 10.1016/j.enconman.2022.116047
M3 - Journal article
AN - SCOPUS:85135501193
SN - 0196-8904
VL - 268
JO - Energy Conversion and Management
JF - Energy Conversion and Management
M1 - 116047
ER -
