A stopping-time reward and chance-constrained SoC penalty embedded in an end-to-end learning framework improves battery reachability of target ranges, raises arbitrage profit, and lowers profit variance under volatile prices.
Task-based end-to-end model learning in stochastic optimization
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An end-to-end learning framework for joint building-data-center integrated energy systems improves operational performance 7-9% over predict-then-optimize baselines and cuts total energy cost ~10% via waste-heat recovery.
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End-to-End Learning-based Operation of Integrated Energy Systems for Buildings and Data Centers
An end-to-end learning framework for joint building-data-center integrated energy systems improves operational performance 7-9% over predict-then-optimize baselines and cuts total energy cost ~10% via waste-heat recovery.