SHORT-TERM PHOTOVOLTAIC POWER PREDICTION METHOD BASED ON MIXED GRAPH NEURAL NETWORK AND GATED RECURRENT UNIT NETWORK

Yin Hao; Li Yidian; Xie Zhifeng; Yu Hui; Zhang Zhan; Wang Yihua

doi:10.19912/j.0254-0096.tynxb.2022-1798

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (3) : 523-532. DOI: 10.19912/j.0254-0096.tynxb.2022-1798

SHORT-TERM PHOTOVOLTAIC POWER PREDICTION METHOD BASED ON MIXED GRAPH NEURAL NETWORK AND GATED RECURRENT UNIT NETWORK

Yin Hao¹, Li Yidian¹, Xie Zhifeng¹, Yu Hui¹, Zhang Zhan¹, Wang Yihua²

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Abstract

In order to extract effective temporal features and connections between non Euclidean domains from a large amount of historical photovoltaic power generation data, a short-term photovoltaic power prediction model based on mixed graph neural network and gated recurrent network is established. The model first generates the K-nearest neighbor graph of meteorological and output data through the K-nearest neighbor classification algorithm, and then uses the graph neural network as an encoder to encode the meteorological and output data to form a time series, and finally outputs the photovoltaic power prediction results through the gated recurrent network and the full connection layer decoding. Through simulation and analysis, the model has better feature mining ability and analysis performance, can better highlight the meteorological and output data characteristics of a certain time node, adapt to the feature changes caused by sudden changes in weather, and thus improve the expression ability of the overall model of photovoltaic prediction.

Key words

graph neural networks / deep learning / photovoltaic power generation / power forecasting / gated recurrent network

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Yin Hao, Li Yidian, Xie Zhifeng, Yu Hui, Zhang Zhan, Wang Yihua. SHORT-TERM PHOTOVOLTAIC POWER PREDICTION METHOD BASED ON MIXED GRAPH NEURAL NETWORK AND GATED RECURRENT UNIT NETWORK[J]. Acta Energiae Solaris Sinica. 2024, 45(3): 523-532 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1798

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