基于多模态数据融合与模糊化的超短期太阳辐照度预测

The intermittency of solar irradiance is the main cause of rapid fluctuations in the power output of photovoltaic (PV) systems. These fluctuations hinder the large-scale integration of solar power generation equipment into the grid, which in turn hinders the process of utilizing solar energy resources to reduce carbon emissions. The main way to solve this dilemma is to achieve high-precision forecasting of solar irradiance. Although various methods exist to forecast the variations of solar irradiance, few focus on fully utilizing multimodal data information and fuzzy method to improve the forecasting performance. Therefore, a forecasting method combining multimodal data fusion and fuzzification is proposed to forecast ultra short-term global horizontal irradiance (GHI). First, a modal conversion method is designed to convert temporal modal data to spatial modal data. Then, the fused data are formed by fusing the converted data with normal and under exposure all-sky images. Subsequently, the fuzzy method is used to generate fuzzy GHI data with low nonlinear features. Last, we utilize deep neural networks to learn potential patterns between fused data and fuzzy GHI data in an end-to-end manner. Our method has been comprehensively validated on data provided by the National Renewable Energy Laboratory, demonstrating its effectiveness, and achieving the highest forecasting accuracy compared to state-of-the-art methods.