Screening of important parameters in optimal design of compressed air energy storage system using an ensemble learning method

Abstract

Accurate prediction of thermophysical properties of compressed air is specifically crucial in optimal design and analyzing performance of a Compressed Air Energy Storage (CAES) system. In this regard, an advanced machine learning method, namely stochastic gradient boosting (SGBTree) technique was examined based on reported data points in the literature for estimation of specific heat ratio of air, water content of atmospheric air, water content of compressed saturated air, viscosity of compressed air as well as thermal conductivity of compressed air. The used ensemble learning method precisely functions for a wide range of temperature, pressure and relative humidity. The SGBTree models estimate the aforementioned target values well, matching to those available in the literature as the correlation coefficient (R) and Mean Relative Absolute Error (MRAE) values are greater than 0.999 and less than 0.007, respectively. Moreover, a comparison was carried out between the outputs of the SGBTree model and two intelligent models namely Artificial Neural Network (ANN) and Least Squares Support Vector Machine (LSSVM) schemes as well as a reported simple empirical correlation for estimating specific heat ratio of air. In terms of accuracy, it is found that the SGBTree model is better than other methods for estimation of specific heat ratio of air.