Improved Error-Based Ensemble Learning Model for Compressor Performance Parameter Prediction

Xinguo Miao, Lei Liu, Zhiyong Wang and Xiaoming Chen ()
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Xinguo Miao: School of Optoelectronic Engineering and Instrumentation Science, Dalian University of Technology, Dalian116024, China
Lei Liu: Design Institute, Shengu Group, Shenyang 110023, China
Zhiyong Wang: Beijing Pipe Co., Ltd., PipeChina Group, Beijing 100020, China
Xiaoming Chen: School of Optoelectronic Engineering and Instrumentation Science, Dalian University of Technology, Dalian116024, China

Energies, 2024, vol. 17, issue 9, 1-12

Abstract: Large compressors have complex structures and constantly changing operating conditions. It is challenging to build physical models of compressors to analyse their performance parameters. An improved error-based stacked ensemble learning prediction model is proposed in this work. This model simplifies the modelling steps in a data-driven manner and obtains accurate prediction results. An enhanced integrated model employs K-fold cross-validation to assign dataset weights based on validation set errors, achieving a 12.4% reduction in average output error. Additionally, the output error of the meta-model undergoes a Box–Cox transformation for error compensation, decreasing the average output error by 14.0%. The Stacking model, combining the above improvements, notably reduces the root-mean-square errors for power, surge, and blocking boundaries by 24.2%, 20.6%, and 23.3%, respectively. This integration significantly boosts prediction accuracy.

Keywords: compressor; performance parameter prediction; stacking integrated learning; error compensation (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: 2024
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