POLAR PHOTOVOLTAIC POWER FORECASTING METHOD BASED ON GA-GNNM

Yang Fan; Shen Ya; Li Dongdong; Li Jie; Wang Zhechao; Lin Shunfu

doi:10.19912/j.0254-0096.tynxb.2020-0768

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (4) : 167-174. DOI: 10.19912/j.0254-0096.tynxb.2020-0768

Topics on Key Technologies for Safety of Electrochemical Energy Storage Systems and Echelon Utilization of Decommissioned Power Batteries

POLAR PHOTOVOLTAIC POWER FORECASTING METHOD BASED ON GA-GNNM

Yang Fan¹, Shen Ya¹, Li Dongdong¹, Li Jie², Wang Zhechao², Lin Shunfu¹

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Abstract

Photovoltaic power forecasting is one of the basics for optimal configuration and coordinated operation of the microgrid. However, due to the extreme conditions of the two seasons in the polar region： polar day and night, and more stormy snow, occasional snow fog weather etc., the polar photovoltaic power is greatly affected, with instability, uncertainty and non-linear characteristics. In order to forecasting the power of polar photovoltaic more accurately and effectively, a polar photovoltaic power forecasting method based on GA-GNNM is proposed. First, cleaning and normalizing abnormal and missing data, useing the maximum correlation minimum redundancy algorithm to select the best feature set, the K-means clustering algorithm is used to cluster the weather types of different seasons, and the relative distance is used to quantify the power with high similarity to the predicted day under different weather types. Construct a gray neural hybrid model （GNNM）, map the differential equations of the gray model into the neural network model, and use genetic optimization algorithm （GA） to optimize the model parameters to avoid local optimization and improve the accuracy of the polar energy prediction algorithm. Finally, the climate and photovoltaic power data of Enksburg Island in Antarctica are used as examples for verification. The results of the example analysis lay a theoretical foundation for the establishment of the fifth new station in the polar region.

Key words

photovoltaic power generation / K-means algorithms / feature selection / predictive analysis / grey neural network modal

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Yang Fan, Shen Ya, Li Dongdong, Li Jie, Wang Zhechao, Lin Shunfu. POLAR PHOTOVOLTAIC POWER FORECASTING METHOD BASED ON GA-GNNM[J]. Acta Energiae Solaris Sinica. 2022, 43(4): 167-174 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0768

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