{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117816"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117816","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A machine learning pipeline for detecting anomalous energy usage in telecommunications sites","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. 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The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Gregory Lee, accepted the attached license on 2022-12-01 at 12:50.","The student, Gregory Lee, submitted this Thesis for approval on 2022-12-01 at 13:04.","This Thesis was approved for publication on 2022-12-05 at 16:04.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18717 on 2023-04-12 at 07:36:45","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A machine learning pipeline for detecting anomalous energy usage in telecommunications sites"]}]}],"canonical_facts":{"dc:contributor":["Caesar, Matthew"],"dc:creator":["Lee, Gregory"],"dc:date":["2022-12","2022-12-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Gregory Lee, accepted the attached license on 2022-12-01 at 12:50.","The student, Gregory Lee, submitted this Thesis for approval on 2022-12-01 at 13:04.","This Thesis was approved for publication on 2022-12-05 at 16:04.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18717 on 2023-04-12 at 07:36:45","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117816"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Gregory Lee"],"dc:subject":["Machine Learning","Telecommunications","Energy"],"dc:title":["A machine learning pipeline for detecting anomalous energy usage in telecommunications sites"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}