SHORT-TERM PHOTOVOLTAIC POWER FORECASTING BASED ON ATTENTION-GRU MODEL

Liu Guohai; Sun Wenqing; Wu Zhenfei; Chen Zhaoling; Zuo Zhiyuan

doi:10.19912/j.0254-0096.tynxb.2020-1202

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (2) : 226-232. DOI: 10.19912/j.0254-0096.tynxb.2020-1202

SHORT-TERM PHOTOVOLTAIC POWER FORECASTING BASED ON ATTENTION-GRU MODEL

Liu Guohai¹, Sun Wenqing¹, Wu Zhenfei², Chen Zhaoling¹, Zuo Zhiyuan¹

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Abstract

In view of the large number of parameters of traditional long-short term memory neural network （LSTM） and lack of important timing information when processing long-term sequences, an Attention-GRU short-term photovoltaic power forecasting model combining attention mechanism and gated recurrent unit （GRU） is proposed. Firstly, a forecasting model for different weather types is established. Then, GRU is used to extract the time series characteristics of photovoltaic power generation and the attention mechanism is introduced to strengthen the attention to important information in the time series input. Finally, a forecasting model for different weather types is established. Simulation results show that the proposed Attention-GRU model has higher forecasting accuracy than the comparison models.

Key words

photovoltaic power generation / power forecasting / neural network / attention mechanism / gated recurrent unit

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Liu Guohai, Sun Wenqing, Wu Zhenfei, Chen Zhaoling, Zuo Zhiyuan. SHORT-TERM PHOTOVOLTAIC POWER FORECASTING BASED ON ATTENTION-GRU MODEL[J]. Acta Energiae Solaris Sinica. 2022, 43(2): 226-232 https://doi.org/10.19912/j.0254-0096.tynxb.2020-1202

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