Resolution Tricks and Disaggregation Tools for Smart Power Metering
Name
Langham-alangham-SM-EECS-2022-thesis.pdf
Description
Thesis PDF
Size
24.74 MB
Format
Adobe PDF
Checksum (MD5)
de2dd5e5fda86abef68135d8a6aa0ba2
Author(s)
Langham, Aaron William
Advisor(s)
Leeb, Steven B.
Donnal, John S.
Green, Daisy H.
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
Abstract
A nonintrusive load monitor (NILM) aims to solve the energy disaggregation problem by incorporating power system analysis, signal processing, and machine learning. This thesis addresses two problems present in state-of-the-art nonintrusive load monitoring research. First, the ability of existing nonintrusive load monitoring techniques and data to generalize is very low, so any data collected for model training needs to be domain-specific. For this reason, this work explores the limits of power signal processing used by deployable NILMs. Secondly, load electrical behavior is almost always assumed to be stationary. Thus, this work presents Adaptive NILM, a set of feature space selection and classification tools useful for nonintrusive load monitoring with limited training data when load operation drifts over time. These techniques are synthesized into a new NILM software package that allows for high-level automation of resolution tracking, feature space evaluation, and adaptive classification. A new NILM hardware implementation, capable of wirelessly integrating data from distributed sensors, is described and demonstrated with case studies.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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