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

Qiao Nan; Zheng Yu; Liu Yandong; Wang Jin; Jiang Botao

doi:10.19912/j.0254-0096.tynxb.2022-1679

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (2) : 59-64. DOI: 10.19912/j.0254-0096.tynxb.2022-1679

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

Qiao Nan^1,2, Jiang Botao^1,2, Zheng Yu^1,2, Liu Yandong^1,2, Wang Jin^1,2

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Abstract

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.

Key words

solar energy / solar radiation / forecasting / deep fuzzy neural network / grasshopper optimization algorithm

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Qiao Nan, Jiang Botao, Zheng Yu, Liu Yandong, Wang Jin. RESEARCH ON GLOBAL SOLAR RADIATION FORECAST BASED ON DEEP FUZZY NEURAL NETWORK[J]. Acta Energiae Solaris Sinica. 2024, 45(2): 59-64 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1679

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