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- W2808060939 abstract "In smart grid applications, such as, demand response and time of day metering, appliance-level load monitoring would play crucial role. It can also help in saving electricity consumptions and reducing electricity bills. This work present power pattern matrix and decision tree based new technique for accurately identifying the home-appliance's uses pattern and disaggregating the various loads from the aggregated electricity consumption data from smart energy meters. The proposed extraction feature, in the form of power pattern matrix, is highly suitable for identification of multi-state devices, which are otherwise difficult to identify just based on power data. Specific screening and classification of extraction features are so developed to. Decision tree machine learning algorithm and multi-label classifications approaches are utilized to get better load disaggregation with lesser computational time. Performance of the proposed technique has been investigated on the basis of precision, classification accuracy, and robustness using experimental data and simulation results." @default.
- W2808060939 created "2018-06-21" @default.
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- W2808060939 date "2017-12-01" @default.
- W2808060939 modified "2023-09-26" @default.
- W2808060939 title "Pattern matrix and decision tree based technique for non-intrusive monitoring of home appliances" @default.
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- W2808060939 doi "https://doi.org/10.1109/icpes.2017.8387403" @default.
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