Photovoltaic Power Forecasting with Missing Values Using VMD, GLTA-Unit and Multi-Scale Temporal Graph Convolution^*

Photovoltaic(PV) power generation forecasting is an important method to solve the inherent volatility and inter-mittency of solar energy. However, traditional research methods often assume that the data is complete or well pre-processed, which does not match the reality of data collection scenarios where data is missing. To this end, We propose M-VGTG, a novel framework that integrates Variational Mode Decompo-sition (VMD), Global-Local Temporal Attention Unit(GLTA-Unit), and Multi-Scale Temporal Graph Convolution to tackle the complex issue of missing values in PV power forecasting. First, to address the problem of modal aliasing, a missing data processing strategy based on VMD is designed. Secondly, a carefully designed GLTA-Unit provides a global-local attention mechanism to capture long-term dependencies and local fluctu-ations, improving the forecasting performance. To address the problem of different positive and negative correlation relation-ships between sequences at different time scales, a multi-scale method is adopted. The GLTA-Unit and Partial Convolution are introduced into the backbone model Temporal Convolutional Network (TCN) to handle temporal features and missing mode updates. Capturing spatial structure is also important. In order to adaptively handle missing modes, this paper uses Adaptive Graph Convolution Network (AGCN) to process spatial features and introduces the latest BiasedGCN module to handle the perception of missing values in the information propagation process. Experiments on real PV power generation datasets verify the effectiveness of the proposed method and demonstrate the potential of this architecture in addressing the unique challenges of PV power gencratlon forecasting.