The installation of smart meters is fast growing to effectively support various smart grid stack holders. Collection and processing of fine-grained metering data is important for proper analysis and decision support. The traditional smart meters are based on standardized and time-invariant tactics to acquire and process the data. This results in the collection, storage, and processing of a huge amount of unneeded data. The focus of this paper is to enhance the contemporary smart meters data acquisition and processing chains. The objective is to attain real-time compression and computational effectiveness to enhance the system performance in terms of data analysis, storage and transmission and to diminish its consumption overhead. In this framework, the signal-piloted event-driven sampling and processing tactics are exploited. The novel adaptive rate techniques are used for data segmentation and extraction of features. Household appliances consumption patterns related features are being classified subsequently. It is realized by employing the mature K-Nearest Neighbor and the Artificial Neural Network classifiers. Results demonstrate a 3.8-fold compression gain and computational effectiveness of the designed solution over traditional counterpart while securing the best classification accuracy of 94.4% for the 6-class appliances dataset.

中文翻译：

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智能电表数据的信号引导处理以实现有效的电器识别

智能电表的安装正在快速增长，以有效支持各种智能电网堆栈持有人。细粒度计量数据的收集和处理对于正确的分析和决策支持非常重要。传统的智能电表基于标准化和时不变的策略来获取和处理数据。这导致收集、存储和处理大量不需要的数据。本文的重点是增强当代智能电表数据采集和处理链。目标是获得实时压缩和计算效率，以提高系统在数据分析、存储和传输方面的性能，并减少其消耗开销。在该框架中，利用了信号引导的事件驱动采样和处理策略。新的自适应速率技术用于数据分割和特征提取。家电消费模式相关的特征也在后续分类中。它是通过采用成熟的K-最近邻和人工神经网络分类器来实现的。结果表明，与传统对应方案相比，所设计的解决方案具有 3.8 倍的压缩增益和计算效率，同时确保 6 类电器数据集的最佳分类准确率达到 94.4%。