In the era of the smart grid, energy disaggregation systems Non-Intrusive Load Monitoring(NILM) become the leading edge in the domain of Demand Side Management(DSM) which intends to provide a path for consumers to grab knowledge about their overall energy usage patterns and to devise appropriate energy strategies with limited smart meter installations. This paper proposes an initiative method on NILM called Non-Intrusive Device Level Load Classification(NIDLC), which classifies the appliances based on the load pattern using a technique of Graph Signal Processing(GSP). The data for load classification is collected from the embedded system built with the raspberry pi and made available in the Applied Electronics Laboratory of the EEE department of Thiagarajar College of Engineering, Madurai. The outcome of the proposed disaggregation is found to be significantly better than the values that were anticipated from the conventional process.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Load Disaggregation using Graph Signal Processing


    Contributors:


    Publication date :

    2023-08-09


    Size :

    727221 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    DISAGGREGATION

    Tom Risen | Online Contents | 2017


    Wave forecasting using disaggregation models

    Kankarej, S. A. / Sharief, M. / Deo, M. C. et al. | British Library Conference Proceedings | 1995


    Sequestration may push milsat disaggregation

    Morring, Frank | Online Contents | 2013


    Origin–destination table disaggregation using biproportional least squares estimation

    Horowitz, A. J. / Transportation Research Board | British Library Conference Proceedings | 2010