LAPSE:2023.28768
Published Article
LAPSE:2023.28768
Systematic Method for the Energy-Saving Potential Calculation of Air-Conditioning Systems via Data Mining. Part I: Methodology
April 12, 2023
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.
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.
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Keywords
data mining, energy saving potential, operational data, Optimization, recognition
Subject
Suggested Citation
Ma R, Yang S, Wang X, Wang XC, Shan M, Yu N, Yang X. Systematic Method for the Energy-Saving Potential Calculation of Air-Conditioning Systems via Data Mining. Part I: Methodology. (2023). LAPSE:2023.28768
Author Affiliations
Ma R: Department of Building Science, Tsinghua University, Beijing 100084, China; School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Yang S: Department of Building Science, Tsinghua University, Beijing 100084, China; Human-Oriented Built Environment Lab, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland [ORCID]
Wang X: Department of Building Science, Tsinghua University, Beijing 100084, China; State Key Laboratory of Air-Conditioning Equipment and System Energy Conservation, Zhuhai 519070, China; Gree Electric Appliances, Inc., Zhuhai 519070, China
Wang XC: State Key Laboratory of Air-Conditioning Equipment and System Energy Conservation, Zhuhai 519070, China; Gree Electric Appliances, Inc., Zhuhai 519070, China
Shan M: Department of Building Science, Tsinghua University, Beijing 100084, China
Yu N: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Yang X: Department of Building Science, Tsinghua University, Beijing 100084, China
Yang S: Department of Building Science, Tsinghua University, Beijing 100084, China; Human-Oriented Built Environment Lab, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland [ORCID]
Wang X: Department of Building Science, Tsinghua University, Beijing 100084, China; State Key Laboratory of Air-Conditioning Equipment and System Energy Conservation, Zhuhai 519070, China; Gree Electric Appliances, Inc., Zhuhai 519070, China
Wang XC: State Key Laboratory of Air-Conditioning Equipment and System Energy Conservation, Zhuhai 519070, China; Gree Electric Appliances, Inc., Zhuhai 519070, China
Shan M: Department of Building Science, Tsinghua University, Beijing 100084, China
Yu N: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Yang X: Department of Building Science, Tsinghua University, Beijing 100084, China
Journal Name
Energies
Volume
14
Issue
1
Article Number
E81
Year
2020
Publication Date
2020-12-25
ISSN
1996-1073
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PII: en14010081, Publication Type: Journal Article
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LAPSE:2023.28768
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https://doi.org/10.3390/en14010081
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