为解决槽式太阳能集热场实际现场工况难以满足现有热性能测试标准的难题,提出一种基于混合神经网络（ALSTM）模型预测槽式太阳能集热场出口温度的方法。首先对中国科学院电工研究所延庆、常州龙腾乌拉特中旗、中广核德令哈、中船乌拉特中旗4处槽式太阳能集热场动态热性能实验数据和气象数据进行预处理与相关性分析,形成训练样本数据和验证样本数据;其次对该网络模型进行训练与优化,最后进行验证,得出上述4处集热场实际出口温度与预测出口温度的最大相对误差分别为3.00%、0.31%、1.45%、1.95%。证明了该模型的预测精度较高,为槽式太阳能集热场实际现场热性能预测与评价提供了一种新的方法。
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.
关键词
槽式太阳能热发电系统 /
集热场出口温度 /
混合神经网络
Key words
trough solar thermal power system /
outlet temperature of heat collector field /
hybrid neural network
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基金
国家重点研发计划（2019YFE0102000）
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