A double DRL architecture selects forecasting models dynamically from a committee and introduces average-reward-convergence early stopping, demonstrating robustness on grocery sales and snack demand datasets.
Combination of short-term load forecasting models based on a stacking ensemble approach.Energy and Buildings, 216:109921
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Designing a double deep reinforcement learning selection tool for resilient demand prediction
A double DRL architecture selects forecasting models dynamically from a committee and introduces average-reward-convergence early stopping, demonstrating robustness on grocery sales and snack demand datasets.