Explainability Assistant: A Conversational XAI Interface for Interpreting Energy Consumption Models· 对话式 XAI 助手：解释能源消耗模型
Energy consumption forecasting relies on increasingly complex machine learning (ML) models, such as Genetic Programming-based symbolic regressors, whose predictions can be difficult for facility managers and building operators to interpret. Explainable Artificial Intelligence (XAI) techniques address this opacity, but traditional XAI dashboards require substantial technical expertise and provide limited flexibility for dynamic, context-aware inquiry. Conversational XAI systems offer a promising alternative; however, previous approaches, such as TalkToModel, were constrained by rigid custom grammars and achieved only 76.8% intent-parsing accuracy. This paper introduces the Explainability Assistant, an open-source conversational XAI system that leverages the function-calling capabilities of
开放源码对话式 XAI 系统帮助解释能源消耗模型。
- 核心方法
- 开发了基于函数调用能力的开放源码对话式 XAI 系统，克服了先前方法中自定义语法的限制，提高意图解析准确性。
- 适合谁读
- 研究者、工程师、设施管理者
- 要解决的问题
- 复杂的能源消耗预测模型难以被设施管理者和操作人员理解，现有 XAI 界面灵活性不足。
- 关键实验
- 实现了更高的意图解析准确率，但具体实验数据未在摘要中提供。
- 主要贡献
- 提供了一个更灵活、更易于使用的对话式 XAI 系统，意图解析准确率达到新的高度。
- 意义与局限
- 提高了能源消耗模型的解释性和可访问性，但可能仍需一定技术背景来充分利用。