一个基于大型语言模型的广大用户推系统
Simona-Vasilica Oprea1, Adela Bâra1
1Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, No. 6 Piaţa Romană, 010374 Bucharest, Romania.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
本研究引入了使用大型语言模型 (LLM) 的推系统,以帮助房主或前期消费者优化家庭能源消耗和成本. 该系统为管理能源和地方能源市场 (LEM) 交易提供了个性化的建议.
科学领域:
- 能源管理 能源管理
- 人工智能的人工智能
- 智能家居是一个智能家居.
背景情况:
- 越来越多地整合智能家庭技术和传感器.
- 作为前期消费者的房主可以获得详细的能源数据和当地能源市场 (LEM) 信息.
- 需要使用决策支持系统来管理复杂的能源数据,以降低成本和保持舒适性.
研究的目的:
- 为前期消费者提出建议系统,以优化能源消耗和成本.
- 为负载调整和LEM交易提供个性化的建议.
- 用特定的前客场景来评估系统的性能.
主要方法:
- 开发一个由大型语言模型 (LLM),Scikit-llm和零射击分类器支持的推系统.
- 评价了两个潜在消费者场景 (5.9千瓦) 具有候选标签:减少,增加,出售和购买.
- 使用相关性能指标与基于内容的过系统进行比较.
主要成果:
- 这套由LLM驱动的系统,基于实时数据,为商提供量身定制的咨询服务.
- 证明了优化能源消耗和LEM参与的潜力.
- 对比分析强调了该系统与传统方法相比的有效性.
结论:
- 基于LLM的推系统可以有效地支持潜在消费者管理家庭能源.
- 个性化推提高了智能家居的成本节约和舒适度.
- 拟议的系统显示了未来能源管理解决方案的前景.
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