A regime-adaptive ensemble with weight-learned neural network and incremental feature engineering reduces minute-scale AI data center load forecasting errors to below 1% on the MIT Supercloud dataset.
Adaptive weighted combination approach for wind power forecast based on deep deterministic policy gradient method
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Regime-Adaptive Weighted Ensemble Learning for Computing-Driven Dynamic Load Forecasting in AI Data Centers
A regime-adaptive ensemble with weight-learned neural network and incremental feature engineering reduces minute-scale AI data center load forecasting errors to below 1% on the MIT Supercloud dataset.