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

被引：0
|
作者：
Sun, Yuxi [1 ]
Liu, Yintao [2 ]
Geng, Guangchao [1 ]
Jiang, Quanyuan [1 ]
机构：
[1] College of Electrical Engineering, Zhejiang University, Hangzhou
[2] College of Engineers, Zhejiang University, Hangzhou
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2024年 / 45卷 / 09期
关键词：
incremental update; neural network; photovoltaic power generation; power forecasting; weather classification;
D O I：
10.19912/j.0254-0096.tynxb.2023-0807
中图分类号：
学科分类号：
摘要：
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. © 2024 Science Press. All rights reserved.
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页码：386 / 393
页数：7
相关论文
共 25 条
  • [1] ZHAO Z M, Et al., Overview of large-scale grid-connected photovoltaic power plants ［J］, Automation of electric power systems, 35, 12, pp. 101-107, (2011)
  • [2] GONG Y F, Et al., An overview of photovoltaic energy system output forecasting technology ［J］, Automation of electric power systems, 40, 4, pp. 140-151, (2016)
  • [3] Systematic literature review of photovoltaic output power forecasting ［J］, IET renewable power generation, 14, 19, pp. 3961-3973, (2020)
  • [4] MINETTE F, BRAUN C，, Et al., Short-term and regionalized photovoltaic power forecasting，enhanced by reference systems，on the example of Luxembourg［J］, Renewable energy, 132, pp. 455-470, (2019)
  • [5] DING M, XU N Z., A method to forecast short-term output power of photovoltaic generation system based on Markov chain［J］, Power system technology, 35, 1, pp. 152-157, (2011)
  • [6] Forecast modeling and performance assessment of solar PV systems ［J］, Journal of cleaner production, 267, (2020)
  • [7] LI Y Z，, HE L, NIU J C., Forecasting power generation of grid-connected solar PV system based on Markov chain［J］, Acta energiae solaris sinica, 35, 4, pp. 611-616, (2014)
  • [8] SHI J E, LIU Y Q，, Et al., Forecasting power output of photovoltaic systems based on weather classification and support vector machines［J］, IEEE transactions on industry applications, 48, 3, pp. 1064-1069, (2012)
  • [9] DING M, WANG L, BI R., A short-term prediction model to forecast output power of photovoltaic system based on improved BP neural network［J］, Power system protection and control, 40, 11, pp. 93-99, (2012)
  • [10] LIU G H，, SUN W Q，, WU Z F，, Et al., Short-term photovoltaic power forecasting based on Attention-GRU model［J］, Acta energiae solaris sinica, 43, 2, pp. 226-232, (2022)
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