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

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.