Very Short-Term Forecasting of Distributed PV Power Using GSTANN

Abstract

Photovoltaic (PV) power forecasting is essential for secure operation of a power system. Effective prediction of PV power can improve new energy consumption capacity, help power system planning, promote development of smart grids, and ultimately support construction of smart energy cities. However, different from centralized PV power forecasts, three critical challenges are encountered in distributed PV power forecasting: 1) lack of on-site meteorological observation, 2) leveraging extraneous data to enhance forecasting performance, 3) spatial-temporal modelling methods of meteorological information around the distributed PV stations. To address these issues, we propose a Graph Spatial-Temporal Attention Neural Network (GSTANN) to predict the very short-term power of distributed PV. First, we use satellite remote sensing data covering a specific geographical area to supplement meteorological information for all PV stations. Then, we apply the graph convolution block to model the non-Euclidean local and global spatial dependence and design an attention mechanism to simultaneously derive temporal and spatial correlations. Subsequently, we propose a data fusion module to solve the time misalignment between satellite remote sensing data and surrounding measured on-site data and design a power approximation block to map the conversion from solar irradiance to PV power. Experiments conducted with real-world case study datasets demonstrate that the prediction performance of GSTANN outperforms five state-of-the-art baselines.

Keywords

• Distributed photovoltaic power forecasting
• graph convolutional networks
• satellite images
• spatial-temporal attention