EnergiQ:一种规范性大型语言模型驱动的智能平台,用于解释设备的能源消耗模式
Christoforos Papaioannou1, Ioannis Tzitzios1, Alexios Papaioannou1
1Management Science and Technology Department, Democritus University of Thrace, 65404 Kavala, Greece.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
使用智能传感器和大型语言模型 (LLM) 提供易于理解的能源洞察力. 这种智能平台可以提高家庭的能源效率,
科学领域:
- 智能家居技术
- 人工智能
- 能源管理系统
背景情况:
- 智能传感器在住宅能源管理中提供了机遇,也带来了复杂性.
- 目前的能源管理系统 (EMS) 缺乏解释性,适应性和用户参与.
- 弥合复杂的能源分析与用户理解之间的差距至关重要.
研究的目的:
- 介绍EnergiQ,一个集成传感器和LLM的智能平台,用于增强能源管理.
- 通过自然语言反来提高用户对能源消耗数据的理解.
- 促进消费者积极参与能源效率和可持续家居实践.
主要方法:
- 集成基于智能插头的物联网传感和时间序列机器学习 (ML).
- 使用XGBoost分类器用于设备识别和CNN-LSTM自动编码器用于异常检测.
- 实现一个LLM推理层与指令调整模型的个性化反.
主要成果:
- 使用基于统计特征的XGBoost实现了高分类精度 (94%).
- 在使用CNN-LSTM自动编码器的各种设备中证明了有效的异常检测.
- 超过91%的人同意专家的节能建议.
结论:
- EnergiQ成功地将复杂的能源数据转化为直观的,可操作的见解.
- 该平台使消费者能够主动管理能源使用并提高效率.
- 整合LLM显著提高了EMS的解释性和用户参与度.
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