基于相似时段匹配与Transformer网络建模的分布式光伏超短期功率预测方法

Distributed Photovoltaic Ultra-short-term Power Forecasting Method Based on Temporal Analog Matching Approach and Transformer Network Modeling

由于缺乏气象数据,分布式光伏在天气骤变场景下预测精度不高,提出了一种基于相似时段匹配与Transformer网络建模的分布式光伏超短期功率预测方法。首先,将相似时段概念由日扩展至更灵活的时间段,并提出了一种历史功率与卫星遥感信息融合的匹配策略,旨在无须依赖气象数据的情况下,高效识别出对预测最为关键的相似功率时段。在此基础上,融合Transformer网络的强大时序建模能力,动态解析多源相似时段中的隐藏关联,深入挖掘功率关键特征信息,从而为天气骤变条件下的分布式光伏系统提供更为精确的超短期功率预测。最后,通过实际分布式光伏功率数据验证了所提方法的有效性。

To address the challenge of low prediction accuracy of distributed photovoltaic(PV)power generation under sudden weather change scenarios due to the lack of meteorological data,this paper proposes a distributed PV ultra-short-term power prediction method based on temporal analog matching approach(TAMA)and Transformer network modeling.Firstly,the concept of similar time periods is extended from days to more flexible time periods,and a matching strategy integrating historical power and satellite remote sensing information is proposed to efficiently identify the most critical time periods of similar power for prediction without relying on meteorological data.Based on this,the powerful temporal modeling capability of the Transformer network is used to dynamically resolve the hidden correlations in multi-source similar time periods,and deeply mine the key features of power,thus providing more accurate ultra-short-term power prediction for distributed PV systems under sudden weather change conditions.Finally,the effectiveness of the proposed method is verified through actual distributed PV power generation data.