开放式协作智能插头用于能源管理
Almir Neto1, Luis Gomes2, Zita Vale2
1Federal Institute of Education, Science and Technology of Maranhão (IFMA), Av. Getúlio Vargas, n 04, Monte Castelo, São Luís, MA, 65030-005, Brazil.
HardwareX
|July 23, 2024
概括
一个新的智能插头,环境意识智能插头 (EnAPlug),使用微型机器学习 (Tiny ML) 来预测冰箱的湿度和运动活动. 这可以通过本地数据存储和智能家居自动化实现增强的能源管理.
科学领域:
- 家电和家庭自动化
- 机器学习应用 机器学习应用
- 能源管理系统 能源管理系统
背景情况:
- 在家用自动化中智能设备的普及需要综合能源管理.
- 了解环境和能源参数之间的相互作用对于高效的设备操作至关重要.
- 现有的智能插头缺乏用于高级分析的本地数据存储功能.
研究的目的:
- 提出一个环境意识智能插头 (EnAPlug),整合机器学习以提高环境意识.
- 为了证明EnAPlug在预测冰箱内部湿度和电机激活方面的能力.
- 通过本地化数据处理来探索增强能源管理的潜力.
主要方法:
- 使用微型机器学习 (Tiny ML) 开发一个环境意识智能插头 (EnAPlug).
- 将EnAPlug应用于冰箱,以实时收集环境和操作数据.
- 实施内部湿度和电机激活的预测模型 (5分钟前预测).
主要成果:
- 湿度预测模型实现了0.055和0.058的根平均平方误差 (RMSE),R2分数为0.97和0.99.
- 运动激活预测模型显示高精度 (94.74%-94.84%) 和F1得分 (0.93-0.97).
- EnAPlug原型成功实现了本地数据存储,使其与传统的智能插头有所区别.
结论:
- EnAPlug原型在环境参数预测和运营预测方面显示出有希望的结果.
- 当地数据存储能力增强了复杂的设备上能源管理策略的潜力.
- 在智能插头中集成Tiny ML为更智能和更高效的家用自动化提供了途径.
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