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Prediction of water wall temperature based on improved grey wolf optimizer and bidirectional long and short term memory network
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Yi ZHAN1, Leihua FENG1, Feng YANG2, Xin ZHONG1
Thermal Power Generation | 2024, 53(1) : 188 - 196
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Thermal Power Generation | 2024, 53(1): 188-196
• Power generation technology forum •
Prediction of water wall temperature based on improved grey wolf optimizer and bidirectional long and short term memory network
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Yi ZHAN1, Leihua FENG1, Feng YANG2, Xin ZHONG1
Affiliations
  • 1.School of Energy and Power Engineering, Changsha University of Science and Technology, Changsha 410114, China
  • 2.HNAC Technology Co., Ltd., Changsha 410006, China
Published: 2024-01-25 doi: 10.19666/j.rlfd.202305076
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An improved grey wolf optimizer (MGWO) is used to optimize BiLSTM to predict water wall temperature. The improved algorithm adopts nonlinear factor adjustment strategy, adaptive position update strategy and dynamic weight modification strategy to improve the global optimization ability of the GWO. The improved grey wolf optimizer is used to optimize the number of hidden layers, learning rate and regularization parameters of the BiLSTM model to improve the prediction accuracy of the model. The data of a power plant in Xinjiang are used for prediction simulation. The results show that, the improved optimizer has higher prediction accuracy, and can predict the change trend of wall temperature when the unit is lifting and lowering load. Compared with the LSTM and BiLSTM models, the average root mean square error of the model reduces by 9.86% and 3.69%, respectively, and the overtemperature of water wall temperature can be predicted in advance, which is of great significance for the prevention of overtemperature of water wall.

water wall  /  prediction of wall temperature  /  bidirectional long and short term memory neural network  /  improved grey wolf optimizer  /  adaptive location updates
Yi ZHAN, Leihua FENG, Feng YANG, Xin ZHONG. Prediction of water wall temperature based on improved grey wolf optimizer and bidirectional long and short term memory network[J]. Thermal Power Generation, 2024 , 53 (1) : 188 -196 . DOI: 10.19666/j.rlfd.202305076
Funding
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  • Natural Science Foundation of Hunan Province(2018JJ3552)
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Year 2024 volume 53 Issue 1
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Article Info
doi: 10.19666/j.rlfd.202305076
  • Receive Date：2023-05-08
  • Online Date：2025-12-25
  • Published：2024-01-25
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  • Received：2023-05-08
Funding
Natural Science Foundation of Hunan Province(2018JJ3552)
Affiliations
    1.School of Energy and Power Engineering, Changsha University of Science and Technology, Changsha 410114, China
    2.HNAC Technology Co., Ltd., Changsha 410006, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202305076
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