An efficient multilayer adaptive self-organizing modeling methodology for improving the generalization ability of organic Rankine cycle (ORC) data-driven model

Abstract

The efficient and accurate model construction of an organic Rankine cycle (ORC) system is the key to its analysis, prediction, and optimization. As a typical multidisturbance nonlinear dynamic system, the ORC system always operates in a nonstationary state. Under uncertain disturbance, accurately identifying the thermal power conversion process state and quickly realizing the accurate association of various mappings are the key considerations of constructing the data-driven model of the ORC system. From the perspective of data selection, parameter association, and structural design, this study proposes a methodology for the efficient multilayer adaptive self-organizing modeling of ORC systems. This methodology can realize efficient autonomous modeling in the whole design process of the data-driven model of the ORC system. Moreover, the proposed methodology can minimize the structural risk of the model by balancing the empirical risk and structural complexity. By taking real data as the test base, the generalization ability and time cost of the data self-selection layer, information self-correlation layer and adaptive self-organizing part of the structure layer are evaluated. Compared with the direct construction of the ORC system data model, the proposed methodology can reduce the model construction time cost by 75.54% and improve the generalization ability by 61.88%. In addition, maximizing the generalization capability with minimum structural risk is an important part of data-driven model construction of ORC systems. In this study, a data-driven model structural reliability assessment approach for ORC systems is proposed. Then, the proposed adaptive self-organizing optimization methodology is verified on the basis of the structural reliability assessment model. The multilayer adaptive self-organizing modeling methodology proposed in this study can provide new ideas and necessary theoretical guidance.