基于多模式增量更新的短期光伏功率预测方法

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

针对传统短期预测模型在特殊天气下准确率低以及未考虑光伏运行环境后续变化的问题,提出一种基于多模式增量更新的短期光伏功率预测方法。在分析气象特征的基础上,根据历史情况预测广义天气类型,在日前根据预测天气类型制定相应的训练函数与数据增强方法,最后基于参数冻结技术对模型进行增量更新,提升了模型对特殊天气的刻画能力以及对后续环境的适应能力。在真实的光伏数据集上进行实验,结果证明该更新方法能有效提高预测准确性。

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.