Systematic Method for the Energy-Saving Potential Calculation of Air Conditioning Systems via Data Mining. Part II: A Detailed Case Study

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, 1-22

Abstract: Increased data monitoring enables the energy-efficient operation of air-conditioning systems via data-mining. The latter is projected to have lesser consumption but more comprehensive diagnosis than traditional methods. Following the companion paper that proposed a systematic method for energy-saving potential calculations via data-mining, this article presents a detailed case study in an ice-storage air-conditioning system by employing the proposed method. Raw data were preprocessed prior to recognizing the constant- and variable-speed devices in the system. Classification and regression tree algorithms were utilized to identify the operating modes of the system. The regression models between the energy-consumption and operating-state parameters of the nine pumps and two chillers were fitted. Furthermore, the constraints pertaining to system operation were summarized. From the results, the particle swarm optimization method was applied to elucidate the benchmark energy cost and the consequent cost savings potential. The cost savings potential for the chiller plant room during the investigation duration of 59 d reached as high as 24.03%. The case study demonstrates the feasibility, effectiveness, and stability of the systematic approach. Further studies can facilitate the development of corresponding control strategies based on the potential analysis results, to investigate better optimization algorithm, and visualize the analysis process.

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
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