A machine learning pipeline for detecting anomalous energy usage in telecommunications sites
Lee, Gregory
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https://hdl.handle.net/2142/117816
Description
Title
A machine learning pipeline for detecting anomalous energy usage in telecommunications sites
Author(s)
Lee, Gregory
Issue Date
2022-12-05
Director of Research (if dissertation) or Advisor (if thesis)
Caesar, Matthew
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
machine learning
telecommunications
energy
Abstract
This thesis presents a general framework for identifying telecommunications sites with abnormally high energy consumption. This pipeline is split into three phases. First, data collected from these telecommunications sites is used to train an ensemble of linear regression models that predict energy consumption for a given site. Next, these models are used to generate predictions for sites in the network. These predictions are compared to their ground truth values to generate a set of potential outlier sites. Each of these potential sites is compared against its nearest neighbors to confidently flag the site as an outlier. Finally, anomalous sites alongside useful visualizations are sent to energy management experts so they can manually review the locations and reduce their energy footprint. A baseline instance of this pipeline is implemented to discuss its strengths and limitations.
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