Investigation of Support Vector Machine and Back Propagation Artificial Neural Network for performance prediction of the organic Rankine cycle system

Shengming Dong, Yufeng Zhang, Zhonglu He, Na Deng, Xiaohui Yu and Sheng Yao

Energy, 2018, vol. 144, issue C, 851-864

Abstract: Low temperature power generation system based on organic Rankine cycle (ORC) has been a popular candidate for low grade heat utilization and recovery. To find a way to predict the performance of the ORC system, the exploration and analyses of the Support Vector Machine (SVM) and Back Propagation Artificial Neural Network (BP-ANN) were carried out. For comparison, both Gauss Radial Basis kernel function (SVM-RBF) and linear function (SVM-LF) have been employed in SVM. Additionally, for the sake of comprehensiveness, two division methods for data set called “random division method” and “blocked division method” were studied. Finally, SVM-LF and BP-ANN demonstrated better stability and higher accuracy for both two division methods and for different testing sets while SVM-RBF showed good results for random division method and disappointing results for blocked division method.

Keywords: ORC; SVM; BP-ANN; Performance prediction; Division method (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:144:y:2018:i:c:p:851-864

DOI: 10.1016/j.energy.2017.12.094

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