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ISSN 0254-0096 CN 11-2082/K

太阳能学报 ›› 2022, Vol. 43 ›› Issue (7): 225-232.DOI: 10.19912/j.0254-0096.tynxb.2020-1079

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基于改进递归深度信念网络的CSP电站短期出力预测

李锦键, 王兴贵, 杨维满, 赵玲霞   

  1. 兰州理工大学电气工程与信息工程学院,兰州 730050
  • 收稿日期:2020-10-13 出版日期:2022-07-28 发布日期:2023-01-28
  • 通讯作者: 王兴贵(1963—),男、硕士、教授、博士生导师,主要从事可再生能源发电系统与控制、微电网技术、电力电子与电力传动等方面的研究。Wangxg689@126.com
  • 基金资助:
    国家自然科学基金(51867016); 国家电网公司科学技术项目(52272817000L); 甘肃省自然科学基金(21JR7RA205)

CSP STATION OUTPUT POWER SHORT-TERM FORECAST BASED ON IMPROVED RNN-DBN

Li Jinjian, Wang Xinggui, Yang Weiman, Zhao Lingxia   

  1. College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
  • Received:2020-10-13 Online:2022-07-28 Published:2023-01-28

摘要: 以预测CSP电站短期出力为目的,首先引入自适应思想对递归深度信念网络的训练算法进行改进,并建立直接法向辐射的短期预测模型。随后提出一种结合静态模型的CSP电站短期出力预测方法。最后进行性能检验,验证了改进递归深度信念网络的可行性,以及CSP电站短期出力预测方法的有效性。研究结果表明:建立的改进递归深度信念网络可提升预测准确性和收敛速度;提出的CSP电站短期出力预测方法可较为准确地预测其短期出力情况。

关键词: 太阳能热发电, 深度神经网络, 信念网络, 递归神经网络, 自适应动量, 直接法向辐射, 短期预测

Abstract: For the purpose of predicting the short-term output power of concentrating solar power (CSP) station, firstly introduce adaptive idea to improve the training algorithm of the recursive deep belief network, and establish a direct normal irradiance short-term prediction model. Secondly, a method for forecasting the short-term output power of CSP station combined with its static model is proposed. Finally, a performance test is carried out to verify the feasibility of improved recursive deep belief network and the effectiveness of the CSP station short-term output power prediction method. The research results show that the established improved recursive deep belief network can improve the prediction accuracy and training speed. Also, the proposed CSP station short-term output power prediction method can predict its output power more accurately.

Key words: CSP, deep neural networks, belief networks, recursive neural networks(RNN), adaptive momentum, direct normal irradiance, short-term forecast

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