NILM-Framework-源码

上传者: 42114041 | 上传时间: 2021-05-24 09:05:22 | 文件大小: 33KB | 文件类型: ZIP
非侵入式负载监控框架 日期: 2015年4月 作者: 蒂博·拉夫里尔(Thibaut Lavril) 版本: 2.0 目的 基于事件检测实现非侵入式负载监视(NILM)的框架。 该框架实现了Hart在[1]中开发的算法和方法。 NILM的目标是将家庭的总消费分解为单独的电器消费(冰箱,空调等)。 为此,使用了机器学习技术(主要是无监督学习)。 算法概述 该算法由不同的步骤组成: 数据加载和预处理:将电表数据(例如,智能电表在不同相位上测得的功率)加载到内存中并进行预处理(采样率,缺失值,离群值)。 事件检测:事件是设备状态变化可能导致的总消耗量变化。 可以通过不同的信号处理算法来检测事件。 事件的聚类:检测到的事件是聚类的,例如,我们尝试将可能来自同一设备状态更改的事件分组在一起。 在那里采用无监督的机器学习算法。 设备建模:利用获得的集群和时间序列分析,构建设备模型。

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