Abstract
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.