Photovoltaic Power Forecasting Scheme Based on Graph Neural Networks through Long- and Short-Term Time Pattern Learning

Jaeseung Lee, Sungwoo Park, Jaeuk Moon, Eenjun Hwang

http://doi.org/10.5626/JOK.2024.51.8.690

As the use of solar energy has become increasingly common in recent years, there has been active research in predicting the amount of photovoltaic power generation to improve the efficiency of solar energy. In this context, photovoltaic power forecasting models based on graph neural networks have been presented, going beyond existing deep learning models. These models enhance prediction accuracy by learning the interactions between regions. Specifically, they consider how the amount of photovoltaic power in a specific region is affected by the climate conditions of adjacent regions and the time pattern of photovoltaic power generation. However, existing models mainly rely on a fixed graph structure, making it difficult to capture temporal and spatial interactions. In this paper, we propose a graph neural networks-based photovoltaic power forecasting scheme that takes into account both long-term and short-term time patterns of regional photovoltaic power generation data. We then incorporate these patterns into the learning process to establish correlations between regions. Compared to other graph neural networks-based prediction models, our proposed scheme achieved a performance improvement of up to 7.49% based on the RRSE, demonstrating its superiority.