Superheated Steam Temperature Prediction Based on Long Short-Term Memory Neural Network Optimized by Novel Whale Optimization Algorithm

FENG Leihua; NIE Chaopeng; GUO Qifeng; YANG Feng; HE Jinqi; MAO Yucheng

doi:10.12096/j.2096-4528.pgt.24127

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Superheated Steam Temperature Prediction Based on Long Short-Term Memory Neural Network Optimized by Novel Whale Optimization Algorithm

Power Generation and Environmental Protection | 更新时间：2026-06-29

- Superheated Steam Temperature Prediction Based on Long Short-Term Memory Neural Network Optimized by Novel Whale Optimization Algorithm
- Power Generation Technology Vol. 46, Issue 6, Pages: 1212-1222(2025)
- 作者机构：
  
  1.长沙理工大学能源与动力工程学院，湖南省长沙市 410114
  2.新疆其亚铝电有限公司，新疆维吾尔自治区阜康市 831100
  3.华自科技股份有限公司，湖南省长沙市 410006
  4.陕西高业能源科技有限公司，陕西省西安市 710061
- 作者简介：
- 基金信息：
  
  Natural Science Foundation of Hunan Province(2018JJ3552);Horizontal Project of Shaanxi Gaoye Energy Technology Co., Ltd(20220030139)
- DOI：10.12096/j.2096-4528.pgt.24127
  CLC： TK 32;TM 621
- Received：08 October 2024，
  
  Revised：2024-12-17，
  
  Published：31 December 2025
- 稿件说明：
移动端阅览
冯磊华,聂超鹏,郭奇峰等.基于改进型鲸鱼算法优化长短期记忆神经网络的过热汽温预测研究[J].发电技术,2025,46(06):1212-1222.

FENG Leihua,NIE Chaopeng,GUO Qifeng,et al.Superheated Steam Temperature Prediction Based on Long Short-Term Memory Neural Network Optimized by Novel Whale Optimization Algorithm[J].Power Generation Technology,2025,46(06):1212-1222.
冯磊华,聂超鹏,郭奇峰等.基于改进型鲸鱼算法优化长短期记忆神经网络的过热汽温预测研究[J].发电技术,2025,46(06):1212-1222. DOI： 10.12096/j.2096-4528.pgt.24127.

FENG Leihua,NIE Chaopeng,GUO Qifeng,et al.Superheated Steam Temperature Prediction Based on Long Short-Term Memory Neural Network Optimized by Novel Whale Optimization Algorithm[J].Power Generation Technology,2025,46(06):1212-1222. DOI： 10.12096/j.2096-4528.pgt.24127.

摘要

目的

火电机组在参与灵活调峰的过程中，机组频繁升降负荷，导致过热汽温波动较大，影响机组运行的安全性和经济性，准确预测过热汽温的变化趋势至关重要。为此，提出一种基于改进型鲸鱼算法(novel whale optimization algorithm，NWOA)优化长短期记忆(long short-term memory，LSTM)神经网络的过热汽温预测模型。

方法

使用主元分析法(principal component analysis，PCA)进行变量选择，以消除初始数据中的冗余变量；采用非线性收敛因子调整策略、自适应权重策略和动态螺旋更新策略对鲸鱼算法(whale optimization algorithm，WOA)进行改进，以提高算法的寻优精度及全局寻优能力；针对新疆某电厂直流锅炉建立过热汽温预测的LSTM模型，并利用改进的鲸鱼算法对该模型进行参数寻优，以解决其最优超参数组合难以选取的问题；运用电厂数据进行预测仿真。

结果

改进后的算法预测精度更高，在机组升降负荷期间，均可以较好预测到过热汽温的变化趋势，模型的平均绝对误差及均方根误差相比于改进前模型分别降低了7.02%和6.22%。

结论

该改进算法有效提高了过热汽温预测模型的精度，为过热汽温控制系统的优化设计提供了基础。

Abstract

Objectives

In the process of participating in flexible peak shaving

thermal power units frequently increase and decrease load

leading to significant fluctuations in superheated steam temperature

which affects the operational safety and economic performance of the units. Accurately predicting the change trend of superheated steam temperature is crucial. Therefore

a superheated steam temperature prediction model based on long short-term memory (LSTM) neural network optimized by the novel whale optimization algorithm (NWOA) is proposed.

Methods

Principal component analysis (PCA) is used for variable selection to eliminate redundant variables in the initial data. The whale optimization algorithm (WOA) is improved by nonlinear convergence factor adjustment strategy

adaptive weighting strategy

and dynamic spiral update strategy to enhance the accuracy and global search ability of the algorithm. An LSTM model for superheated steam temperature prediction is established for a direct current boiler in a power plant in Xinjiang. The improved whale algorithm is used to optimize the parameters of this model

addressing the challenge of selecting the optimal hyperparameter combination. The prediction simulation is carried out using the power plant data.

Results

The improved algorithm demonstrates higher prediction accuracy. It can better predict the change trends of superheated steam temperature during periods of load increase and decrease. The mean absolute error and root mean square error of the model are reduced by 7.02% and 6.22%

respectively

compared to the model before improvement.

Conclusions

The improved algorithm effectively enhances the accuracy of the superheated steam temperature prediction model

providing a basis for the optimal design of the superheated steam temperature control system.

关键词

Keywords

references

国家能源局．国家能源局发布2024年1-8月份全国电力工业统计数据 [J ] ．电力勘测设计， 2024 ( 9 )： 39 ．

National Energy Administration ． National Energy Administration released the statistical data of the national power industry from January to August 2024 [J ] ． Electric Power Survey & Design ， 2024 ( 9 )： 39 ．

姚玉璧，郑绍忠，杨扬，等．中国太阳能资源评估及其利用效率研究进展与展望 [J ] ．太阳能学报， 2022 ， 43 ( 10 )： 524 - 535 ．

YAO Y B ， ZHENG S Z ， YANG Y ， et al ． Progress and prospects on solar energy resource evaluation and utilization efficiency in China [J ] ． Acta Energiae Solaris Sinica ， 2022 ， 43 ( 10 )： 524 - 535 ．

陈文超，杜雯，王帅杰．风力发电对电力系统的影响研究 [J ] ．中国设备工程， 2023 ( 11 )： 135 - 137 ．

CHEN W C ， DU W ， WANG S J ． Study on the influence of wind power generation on power system [J ] ． China Plant Engineering ， 2023 ( 11 )： 135 - 137 ．

杨明杰，胡扬宇，千海霞，等．计及风电、光伏、需求侧响应的藤Copula不确定性的综合能源配网分布鲁棒优化 [J ] ．电测与仪表， 2025 ， 62 ( 10 )： 147 - 155 ． doi: 10.19753/j.issn1001-1390.2025.10.017 http://dx.doi.org/10.19753/j.issn1001-1390.2025.10.017

YANG M J ， HU Y Y ， QIAN H X ， et al ． Distributionally robust optimization for integrated energy distribution network considering vine Copula uncertainty of wind power，photovoltaic and demand side response [J ] ． Electrical Measurement & Instrumentation ， 2025 ， 62 ( 10 )： 147 - 155 ． doi: 10.19753/j.issn1001-1390.2025.10.017 http://dx.doi.org/10.19753/j.issn1001-1390.2025.10.017

王长江，尹浩帆，姜涛，等．计及切换补偿的新能源电力系统暂态电压稳定评估 [J ] ．电工技术学报， 2025 ， 40 ( 17 )： 5487 - 5500 ．

WANG C J ， YIN H F ， JIANG T ， et al ． Transient voltage stability assessment approach for renewable energy power system considering switching compensation [J ] ． Transactions of China Electrotechnical Society ， 2025 ， 40 ( 17 )： 5487 - 5500 ．

王放放，杨鹏威，赵光金，等．新型电力系统下火电机组灵活性运行技术发展及挑战 [J ] ．发电技术， 2024 ， 45 ( 2 )： 189 - 198 ． doi: 10.12096/j.2096-4528.pgt.23079 http://dx.doi.org/10.12096/j.2096-4528.pgt.23079

WANG F F ， YANG P W ， ZHAO G J ， et al ． Development and challenge of flexible operation technology of thermal power units under new power system [J ] ． Power Generation Technology ， 2024 ， 45 ( 2 )： 189 - 198 ． doi: 10.12096/j.2096-4528.pgt.23079 http://dx.doi.org/10.12096/j.2096-4528.pgt.23079

严新荣，胡志勇，张鹏威，等．煤电机组运行灵活性提升技术研究与应用 [J ] ．发电技术， 2024 ， 45 ( 6 )： 1074 - 1086 ． doi: 10.12096/j.2096-4528.pgt.24170 http://dx.doi.org/10.12096/j.2096-4528.pgt.24170

YAN X R ， HU Z Y ， ZHANG P W ， et al ． Research and application of operation flexibility improvement technology for coal-fired power unit [J ] ． Power Generation Technology ， 2024 ， 45 ( 6 )： 1074 - 1086 ． doi: 10.12096/j.2096-4528.pgt.24170 http://dx.doi.org/10.12096/j.2096-4528.pgt.24170

刘云．我国能源电力发展及火电机组灵活性改造综述 [J ] ．洁净煤技术， 2023 ， 29 ( S2 )： 319 - 327 ．

LIU Y ． Overview of energy and power system development and the flexibility retrofit of thermal power units in China [J ] ． Clean Coal Technology ， 2023 ， 29 ( S2 )： 319 - 327 ．

董信光，孙健，孔庆雨，等．超临界350 MW机组直流锅炉深度调峰能力试验 [J ] ．热力发电， 2018 ， 47 ( 7 )： 105 - 112 ．

DONG X G ， SUN J ， KONG Q Y ， et al ． Experimental study on depth peak-load regulation capacity of once-through boiler for a supercritical 350 MW unit [J ] ． Thermal Power Generation ， 2018 ， 47 ( 7 )： 105 - 112 ．

张驰，郭媛，黎明．人工神经网络模型发展及应用综述 [J ] ．计算机工程与应用， 2021 ， 57 ( 11 )： 57 - 69 ．

ZHANG C ， GUO Y ， LI M ． Review of development and application of artificial neural network models [J ] ． Computer Engineering and Applications ， 2021 ， 57 ( 11 )： 57 - 69 ．

杨春来，殷喆，袁晓磊，等．基于神经网络模型的锅炉主蒸汽温度预测控制 [J ] ．锅炉技术， 2023 ， 54 ( 5 )： 30 - 36 ．

YANG C L ， YIN Z ， YUAN X L ， et al ． Predictive control of boiler main steam temperature based on long short-term memory neural network model [J ] ． Boiler Technology ， 2023 ， 54 ( 5 )： 30 - 36 ．

金志远，李胜男，谭鹏，等．基于长短时记忆神经网络的锅炉多参数协同预测模型 [J ] ．热力发电， 2021 ， 50 ( 5 )： 120 - 126 ．

JIN Z Y ， LI S N ， TAN P ， et al ． Multi-parameter collaborative prediction model of boilers based on long-short-term memory neural network [J ] ． Thermal Power Generation ， 2021 ， 50 ( 5 )： 120 - 126 ．

张国斌，郭瑞君，杜荣华，等．基于LSTM预估补偿的火电机组主蒸汽温度控制系统 [J ] ．发电设备， 2023 ， 37 ( 1 )： 51 - 58 ．

ZHANG G B ， GUO R J ， DU R H ， et al ． Main steam temperature control system based on prediction and compensation with LSTM in a thermal power Unit [J ] ． Power Equipment ， 2023 ， 37 ( 1 )： 51 - 58 ．

张文涛，马永光，董子健，等．基于相关向量机的热工参数软测量 [J ] ．热力发电， 2019 ， 48 ( 2 )： 90 - 95 ．

ZHANG W T ， MA Y G ， DONG Z J ， et al ． Soft measurement for thermal parameters based on correlation vector machine [J ] ． Thermal Power Generation ， 2019 ， 48 ( 2 )： 90 - 95 ．

杨婷婷，罗海玉，李浩千，等．基于IWOA优化TCN模型的磨煤机故障预警研究 [J/OL ] ．华北电力大学学报(自然科学版) ， 2024 ： 1 - 9 ．( 2024-01-18 )． https://kns.cnki.net/kcms/detail/13.1212.TM.20240118.1430.002.html https://kns.cnki.net/kcms/detail/13.1212.TM.20240118.1430.002.html ．

YANG T T ， LUO H Y ， LI H Q ， et al ． Research on fault early warning of coal mill based on improved whale algorithm optimizing time convolution network [J/OL ] ． Journal of North China Electric Power University (Natural Science Edition) ， 2024 ： 1 - 9 ．( 2024-01-18 )． https://kns.cnki.net/kcms/detail/13.1212.TM.20240118.1430.002.html https://kns.cnki.net/kcms/detail/13.1212.TM.20240118.1430.002.html ．

张志勇，陆金桂，张猛．基于WOA-BP神经网络的磨煤机出粉量估算 [J ] ．电子测量技术， 2022 ， 45 ( 22 )： 157 - 161 ．

ZHANG Z Y ， LU J G ， ZHANG M ． Estimation of powder output of coal mill based on WOA-BP neural network [J ] ． Electronic Measurement Technology ， 2022 ， 45 ( 22 )： 157 - 161 ．

张宇晨，姜雪松，李春伟，等．基于Bootstrap误差修正的电力负荷短期预测深度学习模型 [J ] ．热力发电， 2023 ， 52 ( 3 )： 121 - 129 ．

ZHANG Y C ， JIANG X S ， LI C W ， et al ． Deep learning model for short-term power load prediction based on Bootstrap error correction [J ] ． Thermal Power Generation ， 2023 ， 52 ( 3 )： 121 - 129 ．

SHAO L ， GUO Q J ， LI C ， et al ． Short-term load forecasting based on EEMD-WOA-LSTM combination model [J ] ． Applied Bionics and Biomechanics ， 2022 ， 2022 ( 1 )： 2166082 ． doi: 10.1155/2022/2166082 http://dx.doi.org/10.1155/2022/2166082

BRODZICKI A ， PIEKARSKI M ， JAWOREK-KORJAKOWSKA J ． The whale optimization algorithm approach for deep neural networks [J ] ． Sensors ， 2021 ， 21 ( 23 )： 8003 ． doi: 10.3390/s21238003 http://dx.doi.org/10.3390/s21238003

LIU L S ， ZHANG R S ． Multistrategy improved whale optimization algorithm and its application [J ] ． Computational Intelligence and Neuroscience ， 2022 ， 2022 ( 1 )： 3418269 ． doi: 10.1155/2022/3418269 http://dx.doi.org/10.1155/2022/3418269

王梓辰，窦震海，董军，等．多策略改进的自适应动态鲸鱼优化算法 [J ] ．计算机工程与设计， 2022 ， 43 ( 9 )： 2638 - 2645 ．

WANG Z C ， DOU Z H ， DONG J ， et al ． Adaptive dynamic whale optimization algorithm based on multi-strategy improvement [J ] ． Computer Engineering and Design ， 2022 ， 43 ( 9 )： 2638 - 2645 ．

李安东，刘升．混合策略改进鲸鱼优化算法 [J ] ．计算机应用研究， 2022 ， 39 ( 5 )： 1415 - 1421 ．

LI A D ， LIU S ． Multi-strategy improved whale optimization algorithm [J ] ． Application Research of Computers ， 2022 ， 39 ( 5 )： 1415 - 1421 ．

SUN Y J ， CHEN Y ． Multi-population improved whale optimization algorithm for high dimensional optimization [J ] ． Applied Soft Computing ， 2021 ， 112 ： 107854 ． doi: 10.1016/j.asoc.2021.107854 http://dx.doi.org/10.1016/j.asoc.2021.107854

张志豪，李军，王林，等．基于内模控制和改进自抗扰控制的主汽温串级控制系统 [J/OL ] ．中国电机工程学报， 1 - 10 [ 2024-04-09 ] ． https://doi.org/10.13334/j.0258-8013.pcsee.231727 https://doi.org/10.13334/j.0258-8013.pcsee.231727 ．

ZHANG Z H ， LI J ， WANG L ， et al ． Cascade control system of main steam temperature based on internal model control and improved active disturbance rejection control [J/OL ] ． Proceedings of the CSEE ， 1 - 10 [ 2024-04-09 ] ． https://doi.org/10.13334/j.0258-8013.pcsee.231727 https://doi.org/10.13334/j.0258-8013.pcsee.231727 ．

张宇帆，艾芊，林琳，等．基于深度长短时记忆网络的区域级超短期负荷预测方法 [J ] ．电网技术， 2019 ， 43 ( 6 )： 1884 - 1892 ． doi: 10.13335/j.1000-3673.pst.2018.2101 http://dx.doi.org/10.13335/j.1000-3673.pst.2018.2101

ZHANG Y F ， AI Q ， LIN L ， et al ． A very short-term load forecasting method based on deep LSTM RNN at zone level [J ] ． Power System Technology ， 2019 ， 43 ( 6 )： 1884 - 1892 ． doi: 10.13335/j.1000-3673.pst.2018.2101 http://dx.doi.org/10.13335/j.1000-3673.pst.2018.2101

MIRJALILI S ， LEWIS A ． The whale optimization algorithm [J ] ． Advances in Engineering Software ， 2016 ， 95 ： 51 - 67 ． doi: 10.1016/j.advengsoft.2016.01.008 http://dx.doi.org/10.1016/j.advengsoft.2016.01.008

YUAN C ， ZHAO D ， HEIDARI A A ， et al ． Artemisinin optimization based on malaria therapy：algorithm and applications to medical image segmentation [J ] ． Displays ， 2024 ， 84 ： 102740 ． doi: 10.1016/j.displa.2024.102740 http://dx.doi.org/10.1016/j.displa.2024.102740

张永，陈锋．一种改进的鲸鱼优化算法 [J ] ．计算机工程， 2018 ， 44 ( 3 )： 208 - 213 ．

ZHANG Y ， CHEN F ． A modified whale optimization algorithm [J ] ． Computer Engineering ， 2018 ， 44 ( 3 )： 208 - 213 ．

武泽权，牟永敏．一种改进的鲸鱼优化算法 [J ] ．计算机应用研究， 2020 ， 37 ( 12 )： 3618 - 3621 ．

WU Z Q ， MU Y M ． Improved whale optimization algorithm [J ] ． Application Research of Computers ， 2020 ， 37 ( 12 )： 3618 - 3621 ．

何庆，魏康园，徐钦帅．基于混合策略改进的鲸鱼优化算法 [J ] ．计算机应用研究， 2019 ， 36 ( 12 )： 3647 - 3651 ．

HE Q ， WEI K Y ， XU Q S ． Mixed strategy based improved whale optimization algorithm [J ] ． Application Research of Computers ， 2019 ， 36 ( 12 )： 3647 - 3651 ．

徐文光，麦鴚，杨开明，等．基于改进WOA参数优化的反应釜PID控制器 [J ] ．现代电子技术， 2024 ， 47 ( 7 )： 150 - 153 ．

XU W G ， MAI G ， YANG K M ， et al ． Reactor PID controller based on improved WOA parameter optimization [J ] ． Modern Electronics Technique ， 2024 ， 47 ( 7 )： 150 - 153 ．

李峰．基于主元分析法的火电厂锅炉故障检测分析 [J ] ．现代制造技术与装备， 2022 ， 58 ( 12 )： 49 - 51 ．

LI F ． Fault detection and analysis of boiler in thermal power plant based on principal component analysis [J ] ． Modern Manufacturing Technology and Equipment ， 2022 ， 58 ( 12 )： 49 - 51 ．

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Economic Model and Key Influencing Factors of Rooftop Distributed Photovoltaic Power Generation in the Whole County

Analysis and Forecast of the Shift From Double Control of Energy Consumption to Double Control of Carbon Emissions in Xinjiang

Summary of Research and Application of Continuous Monitoring Technology for Carbon Emissions From Thermal Power Units

Review of Virtual Power Plant Under the Background of “Dual Carbon”

Optimal Scheduling Strategy of Multi-Energy Complementary Virtual Power Plant Considering Renewable Energy Consumption

Related Author

ZHUANG Kun

WANG Zhonghong

ZHANG Yifan

YIN Wenqian

LEI Maoyun

ZHU Jing

LI Feng

YE Jilei

Related Institution

Huaneng Jiangsu Comprehensive Energy Service Co., Ltd.

School of Energy Science and Engineering, Nanjing Tech University

Huaneng Rizhao Power Plant

School of Electrical Engineering, Xinjiang University, Urumqi 830000, Xinjiang Uygur Autonomous Region

Research Institute of Economics and Technology, State Grid Xinjiang Power Company Limited, Urumqi 830000, Xinjiang Uygur Autonomous Region

AI问答

⁰