Energy efficiency diagnosis is pivotal for ensuring the economic operation of thermal power units. However, existing methodologies often struggle to effectively assimilate unstructured operation and maintenance knowledge and suffer from limited interpretability. While Large Language Models demonstrate formidable reasoning capabilities, their direct deployment in high-stakes industrial environments is impeded by severe challenges, primarily domain knowledge barriers and model hallucinations. To address these issues, we propose an intelligent energy efficiency diagnosis system that synergizes LLMs with visual analytics. First, we construct a collaborative dual- agent workflow: a Diagnostic Agent employs context-aware Retrieval-Augmented Generation to perform bottom-up attribution reasoning along the energy efficiency indicator hierarchy; meanwhile, an Evaluation Agent implements a three-dimensional scoring framework encompassing Data Grounding, Causal Consistency, and Knowledge Application to automatically validate diagnostic conclusions and mitigate hallucinations. Second, to tackle information overload during complex analysis, we design a visual analytics interface featuring Circular Glyphs to intuitively visualize the metric hierarchy and anomaly distribution. Furthermore, an “Evidence Chain” is employed to structurally manage fragmented insights, ultimately facilitating the generation of fully traceable diagnostic reports. Quantitative evaluations and expert interviews demonstrate that our system effectively integrates heterogeneous knowledge, significantly enhances diagnostic efficiency, and bolsters user trust in model outputs.
Wen et al. (Fri,) studied this question.