OUTLET TEMPERATURE PREDICTION OF PARABOLIC TROUGH SOLAR FIELD BASED ON HYBRID NEURAL NETWORK

Yan Lu; Lei Dongqiang; Li Xiao; Xu Li; Dong Jun; Wang Zhifeng

doi:10.19912/j.0254-0096.tynxb.2022-1780

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (5) : 265-273. DOI: 10.19912/j.0254-0096.tynxb.2022-1780

OUTLET TEMPERATURE PREDICTION OF PARABOLIC TROUGH SOLAR FIELD BASED ON HYBRID NEURAL NETWORK

Yan Lu¹, Lei Dongqiang^2,3, Li Xiao¹, Xu Li^2,3, Dong Jun⁴, Wang Zhifeng^2,3

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Abstract

In order to solve the problem that the actual field conditions of the trough solar collector field cannot meet the existing thermal performance test standards, a method of predicting the outlet temperature of the trough solar collector field based on hybrid neural network （ALSTM） model is proposed in this paper. Firstly, the experimental data and meteorological data of dynamic thermal performance of trough solar heat collecting fields in Institute of Electrical Engineering Chinese Academy of Sciences-Yanqing , Changzhou Longteng Wulat Middle Qi, Delingha of China General Nuclear Power Corporation and Wulat Middle Qi of China Ship of Electric Power are preprocessed and correlation analysis is carried out to form training sample data and verification sample data. Secondly, the network model is trained and optimized, and finally verified. It is concluded that the maximum relative errors between the actual and predicted outlet temperatures of the four heat collection fields are 3.00%, 0.31%, 1.45% and 1.95%, respectively. It is proved that the prediction accuracy of this model is high, and it provides a new method for the actual field thermal performance prediction and evaluation of trough solar heat collecting field.

Key words

trough solar thermal power system / outlet temperature of heat collector field / hybrid neural network

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Yan Lu, Lei Dongqiang, Li Xiao, Xu Li, Dong Jun, Wang Zhifeng. OUTLET TEMPERATURE PREDICTION OF PARABOLIC TROUGH SOLAR FIELD BASED ON HYBRID NEURAL NETWORK[J]. Acta Energiae Solaris Sinica. 2023, 44(5): 265-273 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1780

References

[1] 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.
[2] 王志峰, 杜凤丽. 2015~2022年中国太阳能热发电发展情景分析及预测[J]. 太阳能, 2019(11): 5-10, 69.
WANG Z F, DU F L. Scenario analysis and forecast of China′s solar thermal power generation from2015 to 2022[J]. Solar energy, 2019(11): 5-10, 69.
[3] 余洋, 陈庚, 余佳磊, 等. 基于聚类和长短期记忆神经网络的光热电站并网电力预测[J]. 热力发电, 2021, 50(9): 128-136.
YU Y, CHEN G, YU J L, et al.Grid-connected power forecasting of concentrating solar power plants based on clustering and long short-term memory neural network[J]. Thermal power generation, 2021, 50(9): 128-136.
[4] 宋士金. 槽式太阳能热发电计算的模拟仿真[D]. 呼和浩特: 内蒙古工业大学, 2014.
SONG S J.Simulation of a parabolic trough solar thermal power generation system[D]. Hohhot: Inner Mongolia University of Technology, 2014.
[5] 王强. 槽式太阳能集热系统动态性能与试验方法研究[J]. 科技创新与应用, 2022, 12(26): 74-77.
WANG Q.Study on dynamic performance and test method of trough solar heat collection system[J]. Technology innovation and application, 2022, 12(26): 74-77.
[6] 李博, 苑晔. 槽式太阳能热发电系统数值模拟研究[J]. 华电技术, 2018, 40(7): 14-17, 77.
LI B,YUAN Y.Numerical simulation studies of parabolic trough solar thermal power generation system[J]. Huadian technology, 2018, 40(7): 14-17, 77.
[7] ROHANI S, FLURI T P, DINTER F, et al.Modelling and simulation of parabolic trough plants based on real operating data[J]. Solar energy, 2017, 158: 845-860.
[8] SALLABERRY F, VALENZUELA L, PALACIN L G.On-site parabolic-trough collector testing in solar thermal power plants: experimental validation of a new approach developed for the IEC 62862-3-2 standard[J]. Solar energy, 2017, 155: 398-409.
[9] DU B, LUND P D,WANG J, et al.Comparative study of modelling the thermal efficiency of a novel straight through evacuated tube collector with MLR, SVR, BP and RBF methods[J]. Sustainable energy technologies and assessments, 2021, 44: 101029.
[10] 李满峰, 李素萍, 范波. 基于遗传神经网络的太阳能集热器仿真研究[J]. 中国电机工程学报, 2012, 32(5): 126-130.
LI M F, LI S P, FAN B.Research on solar collector simulation based on genetic-BP algorithm[J]. Proceedings of the CSEE, 2012, 32(5): 126-130.
[11] 梅荣. 基于ANN的太阳能集热器热特性仿真研究[D]. 呼和浩特: 内蒙古农业大学, 2011.
MEI R.Modeling analysis of thermal characteristics of solar collectors based on artificial neural network[D]. Hohhot: Inner Mongolia Agricultural University, 2011.
[12] ZAAOUMI A, BAH A, CIOCAN M, et al.Estimation of the energy production of a parabolic trough solar thermal power plant using analytical and artificial neural networks models[J]. Renewable energy, 2021, 170: 620-638.
[13] HENG S Y, ASAKO Y, SUWA T, et al.Transient thermal prediction methodology for parabolic trough solar collector tube using artificial neural network[J]. Renewable energy, 2019, 131: 168-179.
[14] LEI D Q, FU X Q,REN Y C, et al.Temperature and thermal stress analysis of parabolic trough receivers[J]. Renewable energy, 2019, 136: 403-413.
[15] YU Y J, CAO J F, ZHU J Y.An LSTM short-term solar irradiance forecasting under complicated weather conditions[J]. IEEE access, 2019, 7: 145651-145666.
[16] 徐立. 抛物面槽式太阳能集热器热性能动态测试研究[D]. 北京: 中国科学院大学, 2013.
XU L.A study on the dynamic test to determine thermal performance of parabolic trough solar collectors[D]. Beijing: University of Chinese Academy of Sciences, 2013.
[17] 张雲钦, 程起泽, 蒋文杰, 等. 基于EMD-PCA-LSTM的光伏功率预测模型[J]. 太阳能学报, 2021, 42(9): 62-69.
ZHANG Y Q, CHENG Q Z, JIANG W J, et al.Photovoltaic power prediction model based on EMD-PCA-LSTM[J]. Acta energiae solaris sinica, 2021, 42(9): 62-69.
[18] GUO S, PEI H J, WU F, et al.Modeling of solar field in direct steam generation parabolic trough based on heat transfer mechanism and artificial neural network[J]. IEEE access, 2020, 8: 78565-78575.
[19] 栗然, 马涛, 张潇, 等. 基于卷积长短期记忆神经网络的短期风功率预测[J]. 太阳能学报, 2021, 42(6): 304-311.
LI R, MA T, ZHANG X, et al.Short-term wind power prediction based on convolutional long-short-term memory neural networks[J]. Acta energiae solaris sinica, 2021, 42(6): 304-311.