Researchers have developed STOIC, a novel framework for energy demand forecasting that integrates Spatial-Temporal Graph Neural Networks (STGNNs) with foundation models. This approach aims to provide more reliable uncertainty estimates than traditional point forecasting, which is crucial for grid stability and operational planning. STOIC reformulates forecasting residuals into a tabular format for in-context learning with foundation models, enabling calibration without retraining and effectively capturing complex spatial-temporal dependencies. AI
IMPACT Enhances uncertainty quantification in energy systems, potentially improving grid stability and operational planning.
RANK_REASON The cluster contains an academic paper detailing a new method for energy time series forecasting using foundation models and graph neural networks.
- arXiv
- Conformal prediction
- foundation model
- Keivan Faghih Niresi
- Spatial Temporal Graph Neural Networks for Decentralized Control of Robot Swarms
- STOIC
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