SpatialSolar-Net: A Multi-Site Collaborative Framework for Solar Power Forecasting with Adaptive Spatial Correlation Assessment

The increasing penetration of solar power generation poses significant challenges for grid integration due to its inherent variability and intermittency. Existing forecasting approaches treat individual solar installations independently, failing to leverage spatial correlations between geographically proximate sites and lacking adaptive mechanisms for varying environmental conditions. This paper presents SpatialSolar-Net, a novel multi-site collaborative solar power generation forecasting framework that addresses these limitations through adaptive spatial correlation evaluation and dynamic knowledge integration mechanisms. The proposed architecture combines a dual-branch design integrating convolutional neural network-based spatial feature extraction with attention mechanism-based temporal modeling, enhanced by graph neural networks for spatial dependency modeling and an adaptive fusion mechanism that intelligently balances local and spatial information based on real-time correlation strength. This framework significantly enhances renewable energy integration by enabling accurate solar power predictions that support grid stability and optimal resource allocation. Extensive experimental validation demonstrates that SpatialSolar-Net achieves superior performance with Mean Absolute Error of 9.98 kW and Root Mean Square Error of 14.79 kW, representing 12.6% and 10.8% improvements over state-of-the-art methods. Most notably, the framework exhibits exceptional robustness during extreme weather events, achieving a remarkable 64% error reduction during dust storm conditions compared to baseline approaches. The adaptive nature enables efficient deployment across diverse geographical regions while maintaining computational efficiency suitable for practical renewable energy integration.