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Open AccessArticle

Temporal Feature Selection for Multi-Step Ahead Reheater Temperature Prediction

1
School of Comuputer Science and Engineering, Central South University, Changsha 410000, China
2
School of Mechanical Engineering and Automation, Zhejiang Sci-Tech. University, Hangzhou 310000, China
3
School of Automation, Central South University, Changsha 410000, China
*
Author to whom correspondence should be addressed.
Processes 2019, 7(7), 473; https://doi.org/10.3390/pr7070473
Received: 27 March 2019 / Revised: 5 June 2019 / Accepted: 5 June 2019 / Published: 22 July 2019
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Abstract

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. View Full-Text
Keywords: reheat steam temperature; temporal feature selection; delay order prediction; deep neural network; genetic algorithm reheat steam temperature; temporal feature selection; delay order prediction; deep neural network; genetic algorithm
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Gui, N.; Lou, J.; Qiu, Z.; Gui, W. Temporal Feature Selection for Multi-Step Ahead Reheater Temperature Prediction. Processes 2019, 7, 473.

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