SHORT-TERM PHOTOVOLTAIC POWER FORECASTING METHOD BASED ON MULTI-MODE INCREMENTAL UPDATE

Sun Yuxi; Liu Yintao; Geng Guangchao; Jiang Quanyuan

doi:10.19912/j.0254-0096.tynxb.2023-0807

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (9) : 386-393. DOI: 10.19912/j.0254-0096.tynxb.2023-0807

SHORT-TERM PHOTOVOLTAIC POWER FORECASTING METHOD BASED ON MULTI-MODE INCREMENTAL UPDATE

Sun Yuxi¹, Liu Yintao², Geng Guangchao¹, Jiang Quanyuan¹

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Abstract

To address the issues of low accuracy in traditional neural network-based forecasting models under specific weather conditions and the lack of consideration for environmental changes, a short-term photovoltaic （PV） power forecasting method based on multi-mode incremental update is proposed. By analyzing weather features, generalized weather types are forecasted based on historical data. Then, corresponding training methods and data enhancement techniques are developed according to the forecasting weather types for the following day. Finally, by using parameter freezing technology, the model is incrementally updated so that its ability to depict special weather and adapt to subsequent environments is enhanced. Experiments on a real-world PV dataset demonstrate that the proposed method effectively improves forecasting accuracy.

Key words

photovoltaic power generation / power forecasting / neural network / weather classification / incremental update

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Sun Yuxi, Liu Yintao, Geng Guangchao, Jiang Quanyuan. SHORT-TERM PHOTOVOLTAIC POWER FORECASTING METHOD BASED ON MULTI-MODE INCREMENTAL UPDATE[J]. Acta Energiae Solaris Sinica. 2024, 45(9): 386-393 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0807

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