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A deep learning unit commitment method based on confidence intervals
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DOI：10.1016/j.epsr.2026.112908.png)
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Abstract
En 中文
• A unit classification method based on the accuracy of deep learning model outputs and confidence intervals is proposed, categorizing units into confident units, reliable units, hot-start units, and default units. • Different initialization strategies in the mathematical programming (MP) model are designed for different unit types. • The proposed method significantly reduces the solving time of the unit commitment (UC) problem.
Keywords:
deep learning
unit commitment
confidence intervals
mathematical programming
unit classification
Journal
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