AutoGRN: An adaptive multi-channel graph recurrent joint optimization network with Copula-based dependency modeling for spatio-temporal fusion in electrical power systems

Multi-sensor, multi-source information fusion presents significant challenges in complex real-world applications such as power consumption prediction, where existing methods often have limitations in capturing both spatio-temporal features and fully exploit complex relationships among multi-variate features simultaneously. In real-world scenarios, such as complex electrical power system settings, capturing both correlations is important, as spatio-temporal contains vital geographical information and complex inter-series relationships between features. To address these limitations, we propose AutoGRN for enhancing prediction accuracy and efficiency in multi-source spatio-temporal data fusion, with a focus on complex electrical power system settings. AutoGRN integrates a novel adaptive multi-channel attentive framework with copula-based dependency modeling, combining graph neural diffusion convolution and recurrent optimization. The framework automatically learns spatial features, capturing complex correlations among regions, while a sequence encoder extracts temporal patterns, ensuring the acquisition of time series characteristics such as seasonality and trends. High-dimensional spatio-temporal features are then fused through a specially designed multi-channel recurrent graph neural network, incorporating copula functions to model complex dependencies between variables. Extensive experiments on multiple real-world electricity consumption datasets demonstrate that AutoGRN achieves substantial advantages over state-of-the-art benchmarks in multi-variate prediction tasks, showcasing its potential for applications in various multi-sensor, multi-source fusion scenarios, particularly in complex systems requiring simultaneous analysis of spatial and temporal dynamics with intricate inter-variable dependencies. Code is available at https://github.com/AmbitYuki/AutoGRN.