Interpretable AI framework enhances U.S. grid load forecasting under extreme weather

Researchers have developed a new interpretable deep learning framework for electricity load forecasting, designed to enhance U.S. grid resilience during extreme weather events. The system combines Convolutional Neural Network and Transformer branches, with interpretability provided by SHAP analysis. Tested on ERCOT data from 2018-2025, the model achieved significant accuracy improvements, particularly during extreme conditions. AI

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IMPACT Improves grid reliability during extreme weather by providing more trustworthy load forecasts.

RANK_REASON Academic paper detailing a new hybrid deep learning framework for electricity load forecasting.

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• ERCOT
• SHAP
• CNN
• Transformer
• Automated Surface Observing System
• U.S.

• paper
• infra