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This work proposes WorldTS, a world-modeling based forecasting framework that integrates multimodal covariates directly into the forecasting to further improve forecasting performance and employs a two-stage training strategy.
Accurate, reliable, and deployable wind power forecasting is critical for power system dispatch, renewable energy integration, and electricity market operations. Progress in this field hinges on the ability to empirically and comprehensively benchmark forecasting methods. Yet existing benchmarks fall short of supportin...
This work proposes MUSE, a dependency-aware adaptation framework built on a fully frozen pretrained MAE, which performs lightweight refinement at different encoder depths and explicitly models across-period temporal dependencies and within-period periodic dependencies.
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