Non-Parametric Learning for Energy Disaggregation
Jones, Bryan A.
Ball, John E.
Date of Degree
Original embargo terms
MSU Only Indefinitely
Graduate Thesis - Open Access
Electrical and Computer Engineering
Master of Science
James Worth Bagley College of Engineering
Department of Electrical and Computer Engineering
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.
Khan, Mohammad Mahmudur Rahman, "Non-Parametric Learning for Energy Disaggregation" (2018). Theses and Dissertations MSU. 3307.