aceA mix-augmented generation-based intelligent operation and maintenance agent for wind power system with edge deployment
The global transition to clean energy has intensified demand for intelligent wind power systems, yet operational challenges persist due to fragmented knowledge and limited integration across business processes and maintenance management. While large language models (LLMs) exhibit strong reasoning and orchestration capabilities, their application in wind power systems remains constrained by inefficient retrieval-augmented generation (RAG) over heterogeneous knowledge bases, insufficient multimodal data integratio…
A combined cache-augmented and retrieval-augmented method lets a wind-power maintenance agent retrieve knowledge faster and run inference more quickly, including on resource-limited edge devices.
Results are reported from evaluation on the EleQA wind energy dataset, and the text does not describe testing in live wind farm operations.
Evidence
- Peer-reviewedAutonomous Intelligent Systems2026-10-19
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Truvace Impact Record TRV-2026-2055, v1: “A mix-augmented generation-based intelligent operation and maintenance agent for wind power system with edge deployment.” Truvace, 2026-10-20. /record/TRV-2026-2055 (accessed at citation time). sha256 9e63da488bea86e3…
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