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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.
- Graduation Semester
- 2022-12
- Type of Resource
- Thesis
- Handle URL
- https://hdl.handle.net/2142/117816
- Copyright and License Information
- Copyright 2022 Gregory Lee
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Graduate Dissertations and Theses at Illinois PRIMARY
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