Uncertainty in weather, demand, storage, and grid conditions drives renewable-energy curtailment, fossil-fuel peaker use, and disproportionate pollution exposure in communities near conventional power plants. Existing forecasting and optimization tools operate in isolation, rely on centralized data collection, and fail to convert predictions into reliable real-time decisions. This two-year project will develop the first integrated, health-aware artificial-intelligence framework that combines probabilistic forecasting, privacy-preserving federated learning, and grid optimization to coordinate…Zobacz cały przykład →
Abstrakt wniosku grantowego
Dane wejściowe: the future of renewable energy · 2-year project, novel method, societal impact on public health
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