Author:
Lu Yu,Fang Gang,Huang Daoping,Cai Baoping,Chen Hongtian,Liu Yiqi
Abstract
With the ever-increasing growth of energy demand and costs, process monitoring of operational costs is of great importance for process industries. In this light, both financial budget management and local operational optimization supposed to be guaranteed properly. To achieve this goal, a support vector machine recursive feature elimination (SVM-RFE) method together with clustering algorithm was developed to extract features while serving as importance measurements of each input variable for the sequential prediction model construction. Then, the four variants of autoregressive and moving average (ARMA), i.e., ARMA with exogenous input (ARMAX) based on recursive least squares algorithm (RLS), ARMAX based on recursive extended least squares algorithm (RELS), nonlinear auto-regressive neural network (NARNN) and nonlinear auto-regressive neural network with exogenous input (NARXNN), were applied, respectively, to predict the costs incurred in the daily production for process industries. The methods were validated in the Benchmark Simulation Model No.2-P (BSM2-P) and a practical data set about steel industry energy consumption from an open access database (University of California, Irvine (UCI)), respectively. The nonlinear model, NARXNN, was validated to achieve better performance in terms of mean square error (MSE) and correlation coefficient (R), when used for multi-step prediction of the aforementioned datasets with strong nonlinear and coupled characteristics.
Funder
National Natural Science Foundation of China
Horizon 2020 Framework Programme
Basic and Applied Basic Research Foundation of Guangdong Province
Subject
Economics and Econometrics,Energy Engineering and Power Technology,Fuel Technology,Renewable Energy, Sustainability and the Environment