Home energy monitoring by appliance-level information can provide consumers awareness on energy saving. The system can be implemented through a smart meter which requires an efficient data analysis algorithm for providing an accurate energy consumption profile, the purpose for proper home energy management. This article proposes a set of data analysis procedures for extracting appliances power state from its power consumption data. The approach is based on multitarget classification, a new data learning framework for nonintrusive load monitoring. The procedures include: 1) partitioning the appliance power data into an effective number of power states using K-means clustering, and 2) determining the optimal number of power states using the Area Under the ROC Curve index. The design objective is to obtain the optimal predictive performance for identification of the appliance power state which could result in a proper power and energy prediction. Applying the multitarget classification algorithm of RAndom k-labELsets by disjoint subsets with the decision tree, the identification of appliance power state achieved F-score and accuracy values greater than 89% for high-power loads such as A/C and water heater. The normalized error values of power prediction outperformed the use of Factorial Hidden Markov Model and binary state modeling system.

中文翻译：

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一种用于家用电器非侵入式负载监测的能量预测方法

通过家电级信息进行家庭能源监控，可以提高消费者的节能意识。该系统可以通过智能电表来实现，该电表需要高效的数据分析算法来提供准确的能源消耗概况，从而实现适当的家庭能源管理。本文提出了一套数据分析程序，用于从电器的功耗数据中提取电器的功率状态。该方法基于多目标分类，这是一种用于非侵入式负载监控的新数据学习框架。这些过程包括：1) 使用 K 均值聚类将电器功率数据划分为有效数量的功率状态，以及 2) 使用 ROC 曲线下的面积指数确定最佳功率状态数。设计目标是获得最佳预测性能，用于识别设备功率状态，从而进行正确的功率和能量预测。应用随机k-labELsets的多目标分类算法和决策树的不相交子集，家电功率状态的识别达到了F-score，对于空调和热水器等大功率负载的准确度值大于89%。功率预测的归一化误差值优于使用因子隐马尔可夫模型和二元状态建模系统。家电电源状态识别实现了F-score，对于空调、热水器等大功率负载，准确率大于89%。功率预测的归一化误差值优于使用因子隐马尔可夫模型和二元状态建模系统。家电电源状态识别实现了F-score，对于空调、热水器等大功率负载，准确率大于89%。功率预测的归一化误差值优于使用因子隐马尔可夫模型和二元状态建模系统。