Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Tang, Bo

Committee Member

Jones, Bryan A.

Committee Member

Ball, John E.

Date of Degree

8-10-2018

Original embargo terms

MSU Only Indefinitely

Document Type

Graduate Thesis - Campus Access Only

Major

Electrical and Computer Engineering

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

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.

URI

https://hdl.handle.net/11668/20017

Comments

Non-parametric learning||energy disaggregation||Nearest Neighbor||Extended

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