RESEARCH ON GLOBAL SOLAR RADIATION FORECAST BASED ON DEEP FUZZY NEURAL NETWORK

被引：0
作者：
Qiao N. [1 ,2 ]
Jiang B. [1 ,2 ]
Zheng Y. [1 ,2 ]
Liu Y. [1 ,2 ]
Wang J. [1 ,2 ]
机构：
[1] School of Electronics and Information, Xi’an Polytechnic University, Xi’an
[2] Xi’an Key Laboratory of Interconnected Sensing and Intelligent Diagnosis for Electrical Equipment, Xi’an
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2024年 / 45卷 / 02期
关键词：
deep fuzzy neural network; forecasting; grasshopper optimization algorithm; solar energy; solar radiation;
D O I：
10.19912/j.0254-0096.tynxb.2022-1679
中图分类号：
学科分类号：
摘要：
This paper proposes a global solar radiation forecast model based on deep fuzzy neural network. Firstly,Pearson correlation coefficient is used to analyze key influence factors of global solar radiation. Then,the fuzzy neural network modules are repeatedly connected to construct a deep fuzzy neural network model by using the feature extraction advantage of deep learning multiple hidden layers. Moreover,the width and center value of the membership function in this model are optimized by the grasshopper optimization algorithm. Finally,simulation experiments are conducted by using the proposed global solar radiation forecast model based on related data of five meteorological sites. The simulation results show that the proposed model has higher forecast accuracy than other models, and verifies the validity of the model,which meets the requirements of global solar radiation forecast at some sites without radiation monitoring. © 2024 Science Press. All rights reserved.
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页码：59 / 64
页数：5
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