LAPSE:2019.1052
Published Article
LAPSE:2019.1052
Temporal Feature Selection for Multi-Step Ahead Reheater Temperature Prediction
Ning Gui, Jieli Lou, Zhifeng Qiu, Weihua Gui
September 23, 2019
Accurately predicting the reheater steam temperature over both short and medium time periods is crucial for the efficiency and safety of operations. With regard to the diverse temporal effects of influential factors, the accurate identification of delay orders allows effective temperature predictions for the reheater system. In this paper, a deep neural network (DNN) and a genetic algorithm (GA)-based optimal multi-step temporal feature selection model for reheater temperature is proposed. In the proposed model, DNN is used to establish a steam temperature predictor for future time steps, and GA is used to find the optimal delay orders, while fully considering the balance between modeling accuracy and computational complexity. The experimental results for two ultra-super-critical 1000 MW power plants show that the optimal delay orders calculated using this method achieve high forecasting accuracy and low computational overhead. Moreover, it is argued that the similarities of the two reheater experiments reflect the common physical properties of different reheaters, so the proposed algorithms could be generalized to guide temporal feature selection for other reheaters.
Keywords
deep neural network, delay order prediction, Genetic Algorithm, reheat steam temperature, temporal feature selection
Suggested Citation
Gui N, Lou J, Qiu Z, Gui W. Temporal Feature Selection for Multi-Step Ahead Reheater Temperature Prediction. (2019). LAPSE:2019.1052
Author Affiliations
Gui N: School of Comuputer Science and Engineering, Central South University, Changsha 410000, China
Lou J: School of Mechanical Engineering and Automation, Zhejiang Sci-Tech. University, Hangzhou 310000, China
Qiu Z: School of Automation, Central South University, Changsha 410000, China
Gui W: School of Automation, Central South University, Changsha 410000, China
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Journal Name
Processes
Volume
7
Issue
7
Article Number
E473
Year
2019
Publication Date
2019-07-22
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr7070473, Publication Type: Journal Article
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LAPSE:2019.1052
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doi:10.3390/pr7070473
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Sep 23, 2019
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CC BY 4.0
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[v1] (Original Submission)
Sep 23, 2019
 
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Sep 23, 2019
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https://psecommunity.org/LAPSE:2019.1052
 
Original Submitter
Calvin Tsay
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