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Large-language-model-driven multiple algorithm evolution for network-constrained unit commitment model reduction
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AI
DOI：10.1016/j.apenergy.2026.128745.png)
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Abstract
En 中文
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An LLM-driven multiple algorithm evolution method is developed for the NCUC model reduction.
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Code repair strategies and domain-specific knowledge embedding are incorporated to enhance the algorithm evolution method.
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Existing NCUC model reduction algorithms are systematically combined with LLMs to improve generalization, accuracy and computational efficiency.
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The infeasible solution repair algorithm is integrated to ensure solution feasibility after model reduction.
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2.6W
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20.2W
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