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

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    Type
    Book chapter
    Author
    Mian Qaisar, Saeed cc
    Alsharif, Futoon
    Subject
    Smart meter data
    Consumption pattern
    Automatic load identification
    Event-driven processing
    Compression
    Feature extraction
    Machine learning
    Date
    2021
    
    Metadata
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    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.
    Publisher
    Springer Nature
    Book title
    Application of Machine Learning and Deep Learning Methods to Power System Problems
    DOI
    10.1007/978-3-030-77696-1_13
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-030-77696-1_13
    Scopus Count
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