An outlier removal and feature dimensionality reduction framework with unsupervised learning and information theory intervention for organic Rankine cycle (ORC)

The high accuracy prediction model is the basis to investigate the organic Rankine cycle (ORC) system performance. Compared with the traditional thermodynamic model, the data-driven model of ORC system based on artificial neural network (ANN) has obvious advantages in reflecting the strong coupling characteristics of the system. The accuracy of ORC system prediction model depends on the training data, but the outlier removal from the training data has not been fully studied. This paper proposes an unsupervised learning approach for outlier removal in ORC system. Based on this approach, the nonlinear variation relationship between operating parameters and system performance is analyzed. The approach is further compared with the common outliers removal criteria. In addition, reasonable selection of input variables is the basis for the construction of ORC system prediction model, but commonly used selection process cannot effectively filter out the redundant and irrelevant features. A hybrid feature selection algorithm is presented based on Fourier transform and partial mutual information. The effectiveness of the proposed algorithm is compared with principal component analysis. A framework for ORC system outlier removal and feature dimensionality reduction is proposed. The results show that the use of this framework can significantly improve the prediction accuracy of the model. The MAPE and MSE of the model are 6.4 × 10−3% and 3.53 × 10−11, respectively. This framework can provide a direct reference for the construction of data-driven ORC prediction model.