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Non-Parametric Learning for Energy Disaggregation

This thesis work presents a non-parametric learning method, the Extended Nearest Neighbor (ENN) algorithm, as a tool for data disaggregation in smart grids. The ENN algorithm makes the prediction according to the maximum gain of intra-class coherence. This algorithm not only considers the K nearest neighbors of the test sample but also considers whether these K data points consider the test sample as their nearest neighbor or not. So far, ENN has shown noticeable improvement in the classification accuracy for various real-life applications. To further enhance its prediction capability, in this thesis we propose to incorporate a metric learning algorithm, namely the Large Margin Nearest Neighbor (LMNN) algorithm, as a training stage in ENN. Our experiments on real-life energy data sets have shown significant performance improvement compared to several other traditional classification algorithms, including the classic KNN method and Support Vector Machines.

Identiferoai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-4306
Date10 August 2018
CreatorsKhan, Mohammad Mahmudur Rahman
PublisherScholars Junction
Source SetsMississippi State University
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceTheses and Dissertations

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