NOVEL TECHNIQUES FOR THE DETECTION OF ON AND OFF STATES OF APPLIANCES FOR POWER ESTIMATION IN NON-INTRUSIVE LOAD MONITORING

ISARC

*Suman Giri 1 , Po-Hsiang Lai 2 , Mario Bergés 1

1Department of Civil and Environmental Engineering,

5000 Forbes Avenue,

Carnegie Mellon University,

Pittsburgh PA, USA 15213

(Corresponding author: sgiri@andrew.cmu.edu)

2Samsung Telecommunications America,

1301 E Lookout Drive,

Richardson, TX, USA 75082

Non-Intrusive Load Monitoring (NILM) is a method of extracting appliance-level power consumption information from aggregate circuit-level data with the goal of giving users feedback regarding their energy consumption so they can take control of their consumption habits. In this paper, we present a novel algorithm for classification of on and off states of appliances. We compare the performance of our algorithm in on state detection with a pervious paper that evaluated the same dataset and show that it performs up to 13% better. We also present the results of a case study where we collected data for different modes of a cooktop, microwave and dishwasher and used our algorithms to perform power estimation. The error on ten different setups in the test bed ranges from 1% to 32%. We discuss our results and lay out ideas for future work.
Keywords: Power; Algorithms; Algorithm; Estimation; Data; energy; classification; Extraction;
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