Effective Load Pattern Classification by Processing the Smart Meter Data Based on Event-Driven Processing and Machine Learning

Abstract

The technological advancements have evolved the deployment of smart meters. A fine-grained metering data collection and analysis is necessary to bring benefits to multiple smart grid stakeholders. The classical sensing mechanism is time-invariant. Therefore, it results in the collection, transmission, and processing of a large amount of unnecessary data. This work employs the event-driven sensing mechanism to achieve real-time data compression. Afterward, the novel adaptive rate techniques are employed for the data conditioning, segmentation, and extraction of features. The pertinent features regarding the appliances’ consumption patterns are afterward used for their identification. It is realized by employing the mature Support Vector Machine and k-Nearest Neighbor classifiers. Results confirm a 3.4 times compression gain and the computational effectiveness of the suggested solution while securing 95.4% classification precision. It shows the benefits of integrating the proposed method in the realization of current energy efficiency services like enumerated consumption billing, effective load identification, and dynamic load management.