<ul><li>Decision-making in building energy systems relies on accurate predictive models.</li><li>Time-series foundation models (TSFMs) leverage prior knowledge from diverse datasets for accurate probabilistic forecasting.</li><li>Study compares full fine-tuning and parameter-efficient fine-tuning approaches for building energy forecasting.</li><li>Results show that fine-tuned TSFMs outperform deep forecasting models and improve decision-making for energy efficiency and sustainability.</li></ul>

Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models

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