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Learning from past decisions: A framework for evaluating wind power locations
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DOI：10.1016/j.indic.2026.101260.png)
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Abstract
En 中文
• Preference-learning framework supports wind farm site assessment. • Applied to 216 wind projects in Rio Grande do Norte, Brazil. • Three decision makers built collective decision rules across five iterations. • Unsorted areas fell from 8% to 4%, improving classification coverage. • Results are interpretable and reproducible.
Keywords:
Wind power
Preference learning
Rough set
Decision rules
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