Abstract
The convergence of advancements of Internet of Things (IoT) capabilities, cou-pled with the accessibility of inexpensive and user-friendly sensors, has propelled the emergence of various new domains, and one such domain is Non-Intrusive Load Monitoring (NILM). A pivotal aspect of these technological advancements is the identification of appliances through the analysis of disaggregated power consumption signatures. The length of these signatures is contingent upon the frequency of data collection, where higher frequencies correspond to lengthier time series. To address this, we introduce a novel dynamic time series data reduction methodology, tailored to efficiently extract regions of interest from extended time series data. Subsequently, the efficacy of appliance classification using these extracted sub-ranges is assessed through the utilization of Matrix Profile techniques. The experimental validation is conducted utilizing the Plug-Load Appliance Identification Dataset, thus offering a concrete empirical basis for our approach's evaluation and verification. The proposed approach has successfully improved the overall accuracy of identifying the appliances in PLAID dataset with a significant margin as compared to the baseline approach.
Published Version
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