Systematic Method for the Energy-Saving Potential Calculation of Air-Conditioning Systems via Data Mining. Part I: Methodology

Rongjiang Ma, Shen Yang, Xianlin Wang, Xi-Cheng Wang, Ming Shan, Nanyang Yu and Xudong Yang
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Rongjiang Ma: Department of Building Science, Tsinghua University, Beijing 100084, China
Shen Yang: Department of Building Science, Tsinghua University, Beijing 100084, China
Xianlin Wang: Department of Building Science, Tsinghua University, Beijing 100084, China
Xi-Cheng Wang: State Key Laboratory of Air-Conditioning Equipment and System Energy Conservation, Zhuhai 519070, China
Ming Shan: Department of Building Science, Tsinghua University, Beijing 100084, China
Nanyang Yu: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Xudong Yang: Department of Building Science, Tsinghua University, Beijing 100084, China

Energies, 2020, vol. 14, issue 1, 1-15

Abstract: Air-conditioning systems contribute the most to energy consumption among building equipment. Hence, energy saving for air-conditioning systems would be the essence of reducing building energy consumption. The conventional energy-saving diagnosis method through observation, test, and identification (OTI) has several drawbacks such as time consumption and narrow focus. To overcome these problems, this study proposed a systematic method for energy-saving diagnosis in air-conditioning systems based on data mining. The method mainly includes seven steps: (1) data collection, (2) data preprocessing, (3) recognition of variable-speed equipment, (4) recognition of system operation mode, (5) regression analysis of energy consumption data, (6) constraints analysis of system running, and (7) energy-saving potential analysis. A case study with a complicated air-conditioning system coupled with an ice storage system demonstrated the effectiveness of the proposed method. Compared with the traditional OTI method, the data-mining-based method can provide a more comprehensive analysis of energy-saving potential with less time cost, although it strongly relies on data quality in all steps and lacks flexibility for diagnosing specific equipment for energy-saving potential analysis. The results can deepen the understanding of the operating data characteristics of air-conditioning systems.

Keywords: energy saving potential; data mining; recognition; optimization; operational data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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