考虑信息时移的分布式光伏机理-数据混合驱动短期功率预测

Hybrid Mechanism-Data-Driven Short-term Power Forecasting of Distributed Photovoltaic Considering Information Time Shift

分布式光伏短期功率预测缺乏同时空气象数据。传统方法直接借助邻近集中式光伏站点数据进行功率预测,忽略了地理位置偏移带来的气象信息时移,难以满足预测精度要求。文中提出了一种考虑气象信息时移的混合预测方法。在机理驱动模型中,采用最优时移对气象数据进行偏移修正;在数据驱动模型中,引入时间模式注意力机制削弱气象数据偏移的影响。然后,通过Stacking集成学习框架将两种方法进行融合,形成机理-数据混合驱动模型,进一步提高预测稳定性及准确率。基于分布式光伏和公共气象站点实际数据进行的案例分析表明,所提方法能够有效利用偏移地理位置的气象数据,实现更高精度的分布式光伏发电功率预测。

The short-term power forecasting of distributed photovoltaic lacks the same spatio-temporal meteorological data.Traditional methods of power forecasting directly use the data of adjacent centralized photovoltaic stations,ignore the time shift of meteorological information caused by geographical location offset,which is difficult to meet the requirements of forecasting accuracy.A hybrid forecasting method considering the time shift of meteorological information is proposed.In the mechanismdriven model,the optimal time shift is used to modify the meteorological data offset.In the data-driven model,the temporal pattern attention(TPA)mechanism is introduced to weaken the impact of meteorological data offset.Then,the two methods are fused through the Stacking ensemble learning framework to form a hybrid mechanism-data-driven model to further improve the forecasting stability and accuracy.The case analysis based on the actual data of the distributed photovoltaic and public meteorological stations shows that the proposed method can effectively use the meteorological data of geographical location offset to achieve higher accuracy of power generation forecasting for distributed photovoltaics.