Evaluation of hybrid forecasting methods for organic Rankine cycle: Unsupervised learning-based outlier removal and partial mutual information-based feature selection. (1st April 2022)

• Record Type: Journal Article
• Authors: Ping, Xu; Yang, Fubin; Zhang, Hongguang; Xing, Chengda; Zhang, Wujie; Wang, Yan
• Is Part Of: Applied energy. Volume 311(2022)
• Publication Date: 2022-04-01
• Subjects: Organic Rankine cycle -- Outlier removal -- Feature selection -- Unsupervised learning -- Partial mutual information; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.118682
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: The nonlinear characteristics of real organic Rankine cycle (ORC) data are analysed. An unsupervised learning-based algorithm is proposed for outlier removal. A partial mutual information-based feature selection is performed. Our hybrid method has superior performance in ORC forecasting. Abstract: The construction of organic Rankine cycle (ORC) system model is the key to system performance analysis and prediction. However, traditional analysis methods have obvious limitations in constructing strong coupling relationship between operating parameters and performance due to the complex thermal power conversion process of ORC system. First, this study systematically analyzes the nonlinear relationship between ORC system operating parameters and performance by using unsupervised learning and bilinear interpolation algorithm. Compared with the traditional thermodynamic modeling method, the artificial neural network (ANN) has obvious advantages in constructing the mapping relationship of ORC system. However, the ORC system prediction model still has the defects of low accuracy, poor robustness, and high time cost due to the absence of outlier removal and feature dimensionality reduction. A hybrid algorithm for ORC system prediction model construction is proposed on the basis of the data characteristics, information theory and unsupervised learning. This algorithm can remove outliers and reduce the dimensionality of features in ORC system simultaneously. Then, the effectiveness of outlier removal, feature dimensionality reduction, and overall performance of the hybrid algorithm is verified. The mean squared error and mean absolute percentage error of the model is 1.64 × 10 −11 and 5.1 × 10 −3 %. Compared with other algorithms, the hybrid algorithm suitable for ORC system has improved in accuracy and time cost. The accuracy of the hybrid algorithm is improved by 5.56% at least. The time cost of the hybrid algorithm is reduced by at least 17.05%. The hybrid algorithm can provide direct guidance for constructing ANN model of ORC system.