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Indexed by:期刊论文

Date of Publication:2012-06-15

Journal:INFORMATION SCIENCES

Included Journals:SCIE、EI、Scopus

Volume:193

Page Number:104-114

ISSN No.:0020-0255

Key Words:Steam system; Data-driven; Time series prediction; Bayesian ESN

Abstract:The steam system is one of the main energy systems in steel industry, and its operational scheduling plays a crucial role for energy utility and resources saving. For a reasonable resources operation, the accurate prediction of steam flow is required. Considering the large amount of production data in energy system, a data-driven based model is proposed to perform a time series prediction for steam flow, in which a Bayesian echo state network (ESN) is established. This method combines Bayesian theory with ESN to obtain optimal output weight via maximizing the posterior probability density of the weights to avoid over-fitting in the training process of sample data. To pursue optimized hyper-parameters in the proposed Bayesian ESN, the evidence framework based on sample data is further adopted in this work. Experimental results using the real production data from Shanghai Baosteel show the validity and practicality of the proposed data-driven based model in providing scientific decision guidance for the steam system. Published by Elsevier Inc.