Modeling of Spatial Distribution Characteristics of High Proportion Renewable Energy Based on Complex Principal Component Analysis

The optimal allocation of global energy resources is a cardinal direction of the energy system, and the forecasting of renewable energy generation power prediction is the basis of energy interconnection. In power forecasting, it is necessary to integrate multiple information to improve the accuracy. Thus, bringing data that has no impact on the outcome and leads to computationally intensive data. Conventional principal component analysis (PCA) can downscale data with no temporal order. However, the data of meteorological parameters are with high temporal and spatial resolution. Therefore, it needs to be extended to complex principal component analysis (CPCA). Simultaneously, the unknown historical output of the regional power grid poses difficulties in predicting the generation power. This paper extracts the spatio-temporal features of renewable energy from typical local grids in the world based on CPCA. Through the interconnection relationship between different regions, this paper establishes a renewable energy generation prediction model. The validity and accuracy of the model are verified in MATLAB with domestic and foreign regional power grids as examples.