Small Area PV Ultra-short-term Prediction Method Using Stratified Digraph and Dynamic Spatial-temporal Correlation
The accurate prediction of photovoltaic power generation in small areas has become a technical bottleneck for the precise control and operation of high-penetration multi-zone systems.Currently,photovoltaic power generation prediction methods need more consideration of the photovoltaic cluster effect in small areas of the station area and ignore the inherent causality and dynamic correlation of input variables.To solve these problems,a small area PV prediction method based on a hierarchical digraph and dynamic graph convolutional recurrent network(DGCRN)is proposed.Firstly,considering the unidirectional relationship between output data and numerical weather prediction(NWP),a hierarchical digraph with a causal relationship is generated.Secondly,a dynamic graph is generated at each time step according to node attributes,which is organically combined with a pre-defined static graph to capture the dynamic spatio-temporal correlation between nodes.Finally,the graph structure with dynamic spatiotemporal correlation is used for model training.The experimental results show that the DGCRN model can capture the causal law between multiple parameters,extract the short-term dynamic characteristics of photovoltaic power,and have superior performance in predicting photovoltaic power in a small area with multiple nodes.
万方数据
维普
