Tyan-WP is a pretrained wind power foundation model that outperforms site-specific TSMs and generic LTSMs in zero-shot ultra-short-term probabilistic forecasting on U.S. and U.K. sites via static embeddings and PAMF module.
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Adaptive clustering of subproblems in Benders decomposition yields grouped cuts that outperform standard multi-cut formulations for energy CEMs under weak inter-temporal coupling, with gains largest in big systems with short horizons.
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Tyan-WP: A Wind Power Foundation Model for Ultra-Short-Term Probabilistic Forecasting
Tyan-WP is a pretrained wind power foundation model that outperforms site-specific TSMs and generic LTSMs in zero-shot ultra-short-term probabilistic forecasting on U.S. and U.K. sites via static embeddings and PAMF module.
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Clustering-enhanced adaptive Benders decomposition for energy systems planning optimization
Adaptive clustering of subproblems in Benders decomposition yields grouped cuts that outperform standard multi-cut formulations for energy CEMs under weak inter-temporal coupling, with gains largest in big systems with short horizons.