Accurate Multi-Site Photovoltaic Power Forecasting Using Dynamic Graph Convolution and Bidirectional Multi-Scale Attention
TL;DR
Experimental results on various publicly available PV power datasets show that BiMS-DGCN consistently outperforms several state-of-the-art forecasting methods, thereby improving the accuracy of multi-site photovoltaic power forecasting.
Abstract
Accurate multi-site photovoltaic (PV) power forecasting plays a pivotal role in enhancing smart grid operation and facilitating large-scale renewable energy integration. However, existing forecasting approaches often struggle to simultaneously capture the dynamic spatial interactions among geographically distributed PV plants and the diverse temporal dependencies embedded in PV power generation data. To overcome these limitations, this paper proposes a Bidirectional Multi-Scale Dynamic Graph Convolutional Network (BiMSDGCN). The proposed framework integrates two key components. First, a Dynamic Graph Convolutional Network (DGCN) is designed to adaptively learn a time-dependent adjacency matrix to capture evolving spatial correlations among PV sites. Second, a Bidirectional Multi-Scale Attention (BiMS) is developed to model both cross-scale and intra-scale dependencies, enabling more comprehensive spatiotemporal feature extraction and information fusion. Experimental results on various publicly available PV power datasets show that BiMS-DGCN consistently outperforms several state-of-the-art forecasting methods. Ablation studies further verify that both DGCN and BiMS play critical roles in modeling complex spatiotemporal dependencies, thereby improving the accuracy of multi-site photovoltaic power forecasting.