On the Exploration of Graph State-Space Models for Spatio-Temporal Renewable Energy Forecasting

In the face of increasing demand for accurate energy forecasting, State-Space Models have arisen as an effective method for spatiotemporal prediction in renewable energy systems. This preliminary study explores the application of graph-based state-space models to renewable energy datasets, aiming at enhancing the accuracy and reliability of energy forecasts. We investigate the performance of these models in capturing the complex spatiotemporal dependencies inherent in energy production data from diverse renewable sources, represented by solar and wind power plants. We employ the Spatial-Temporal Graph Mamba model as a benchmark for validating the state-space model mechanism in the forecasting problem. Our experiments indicate that state-space models offer promising capabilities in forecasting energy output with improved precision over traditional methods and reduced computational cost. We also discuss the implications of these findings for future research and the potential for integrating state-space models into real-world energy management systems.