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Non-Intrusive Residential Load Monitoring System Using Appliance: Based Energy Disaggregation Models

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Abstract

With large scale implementation of smart metering technology, the implications demand to account the energy consumption at every node of the system, but the device scalability is of great concern. Energy Disaggregation serves the purpose of finding the appliance level energy consumption from the aggregate energy, which helps to unlock the interactions between the devices through load characterization. This paper proposes a decision tree-based approach for identifying the device operations thereby effectively categorizing the load. A balanced data learning approach is adopted for data processing to eliminate the outliers during training and testing phase of classifier and also improve the classifier performance. The proposed model was evaluated using Reference Energy DisaggregationDataset (REDD) and Retrofit DecisionSupport Tools for UK Homes using Smart Home Technology (REFIT) Dataset. The performance metrics has been obtained for individual appliance using decision tree, naïve bayes and k-nearest neighbor classifiers andanalysed for validation. The proposed disaggregation approach has proven to give promising results in terms of better and accurate detection of appliance operation. The load monitoring system is developed to detect the appliance operation by sensing the voltage, current and power data at defined sampling rate of frequency. Even with large training dataset, the results obtained during testing phase with unseen dataset were viable for further allegations of proposed load model.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Kamaraj College of Engineering and Technology, Madurai, India

    Devie Paramasivam Mohan & Kalyani Sundaram

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  1. Devie Paramasivam Mohan

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  2. Kalyani Sundaram

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Correspondence toDevie Paramasivam Mohan.

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Paramasivam Mohan, D., Sundaram, K. Non-Intrusive Residential Load Monitoring System Using Appliance: Based Energy Disaggregation Models.J. Electr. Eng. Technol.18, 3783–3798 (2023). https://doi.org/10.1007/s42835-023-01475-2

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