{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/106419"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/106419","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Polynomial approximations for fast predictive analysis of infrastructure systems: Applications to power and transportation systems","abstract":"The student, Negin Alemazkoor, accepted the attached license on 2019-08-21 at 11:03.","abstract_html":"The student, Negin Alemazkoor, accepted the attached license on 2019-08-21 at 11:03.","abstract_has_math":false,"creators":["Alemazkoor, Negin"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Meidani, Hadi","Work, Daniel","Lehe, Lewis","Kumar, Praveen","Spencer, Bill"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-03-02T22:38:34Z","date_published":"2020-03-02T22:38:34Z","updated_at":"2026-07-22T22:24:47Z","subjects":["Infrastructure systems, Predictive analysis"],"languages":["en"],"rights":["Copyright 2019 Negin Alemazkoor"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/106419","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Meidani, Hadi","Work, Daniel","Lehe, Lewis","Kumar, Praveen","Spencer, Bill"]},{"key":"dc:creator","label":"Author","values":["Alemazkoor, Negin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-03-02T22:38:34Z","2022-03-03T10:15:27Z","2019-08-23","2019-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Infrastructure systems, Predictive analysis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Negin Alemazkoor"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/106419"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The student, Negin Alemazkoor, accepted the attached license on 2019-08-21 at 11:03.","The student, Negin Alemazkoor, submitted this Dissertation for approval on 2019-08-21 at 11:17.","This Dissertation was approved for publication on 2019-08-23 at 09:36.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14431 on 2020-02-28 at 17:35:12","Made available in DSpace on 2020-03-02T22:38:34Z (GMT). No. of bitstreams: 2 ALEMAZKOOR-DISSERTATION-2019.pdf: 3615675 bytes, checksum: df90f3df1f18e0381049169d905f322f (MD5) LICENSE.txt: 4213 bytes, checksum: 418cbb52e01200eb6a3d5874a8a5f49e (MD5) Previous issue date: 2019-08-23","Embargo set by: Seth Robbins for item 113963 Lift date: 2022-03-02T22:39:04Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 113963 on 2022-03-03T10:15:27Z.","Infrastructure systems are complex networks with inherent sources of uncertainty. Optimal operation of these systems directly affects the welfare of society. Accurate analysis and predictions for infrastructure systems are vital to achieve optimal management and operation. Data for predictive analysis can be from different sources, including computationally expensive system simulations or sensors placed within the system. For a reliable predictive analysis, it is necessary to (a) incorporate significant uncertainty in behavior of the system induced by inherent variability of system components, and (b) capture the changes within the system and adjust the predictions accordingly. This study aims to address some of the main challenges regarding these two pillars of a reliable predictive analysis for infrastructure systems. Specifically, consider power transmission or distribution systems, where computationally expensive power flow simulations must be run to evaluate the future state of the system. Conventionally, uncertain variables, such as power consumption, are treated as deterministic variables. This can result in unreliable predictions and consequently suboptimal decisions. On the other hand, quantifying the uncertainty in the system's state using sampling approaches may require thousands of simulations and can be computationally intractable. To reduce the computational burden, full scale simulations should be replaced with analytical surrogates such as polynomial functions, radial basis functions, and Gaussian processes. Accuracy of these surrogates directly affects the accuracy of system analysis and the optimality of the decisions made based on the analysis. In this dissertation, we focus on polynomial surrogates and develop innovative methodologies to improve the accuracy of the polynomial surrogates. We use several numerical examples to validate the efficiency and accuracy of the proposed methodologies. Also, as demonstration on the application side, we apply the developed methodologies to a power distribution system with various uncertainty, such as power generation and consumption uncertainty. The results demonstrate that our proposed approaches substantially reduce the computational cost associated with probabilistic power flow analysis and probabilistic system control. Additionally, for the cases that data is constantly streaming from the sensors within the system, a computationally fast online predictive model is introduced, that is capable of adjusting the predictions once system faces significant disruptions. The efficiency and accuracy of the proposed approach is demonstrated using a real-world extreme scenario, namely the Woolsey wildfire in California, following which traffic patterns significantly changed. Specifically, we study traffic conditions in locations close to the wildfire and show that the proposed approach can capture and accurately predict the post-disaster changes.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-12-01"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Polynomial approximations for fast predictive analysis of infrastructure systems: Applications to power and transportation systems"]}]}],"canonical_facts":{"dc:contributor":["Meidani, Hadi","Work, Daniel","Lehe, Lewis","Kumar, Praveen","Spencer, Bill"],"dc:creator":["Alemazkoor, Negin"],"dc:date":["2020-03-02T22:38:34Z","2022-03-03T10:15:27Z","2019-08-23","2019-12"],"dc:description":["The student, Negin Alemazkoor, accepted the attached license on 2019-08-21 at 11:03.","The student, Negin Alemazkoor, submitted this Dissertation for approval on 2019-08-21 at 11:17.","This Dissertation was approved for publication on 2019-08-23 at 09:36.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14431 on 2020-02-28 at 17:35:12","Made available in DSpace on 2020-03-02T22:38:34Z (GMT). No. of bitstreams: 2 ALEMAZKOOR-DISSERTATION-2019.pdf: 3615675 bytes, checksum: df90f3df1f18e0381049169d905f322f (MD5) LICENSE.txt: 4213 bytes, checksum: 418cbb52e01200eb6a3d5874a8a5f49e (MD5) Previous issue date: 2019-08-23","Embargo set by: Seth Robbins for item 113963 Lift date: 2022-03-02T22:39:04Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 113963 on 2022-03-03T10:15:27Z.","Infrastructure systems are complex networks with inherent sources of uncertainty. Optimal operation of these systems directly affects the welfare of society. Accurate analysis and predictions for infrastructure systems are vital to achieve optimal management and operation. Data for predictive analysis can be from different sources, including computationally expensive system simulations or sensors placed within the system. For a reliable predictive analysis, it is necessary to (a) incorporate significant uncertainty in behavior of the system induced by inherent variability of system components, and (b) capture the changes within the system and adjust the predictions accordingly. This study aims to address some of the main challenges regarding these two pillars of a reliable predictive analysis for infrastructure systems. Specifically, consider power transmission or distribution systems, where computationally expensive power flow simulations must be run to evaluate the future state of the system. Conventionally, uncertain variables, such as power consumption, are treated as deterministic variables. This can result in unreliable predictions and consequently suboptimal decisions. On the other hand, quantifying the uncertainty in the system's state using sampling approaches may require thousands of simulations and can be computationally intractable. To reduce the computational burden, full scale simulations should be replaced with analytical surrogates such as polynomial functions, radial basis functions, and Gaussian processes. Accuracy of these surrogates directly affects the accuracy of system analysis and the optimality of the decisions made based on the analysis. In this dissertation, we focus on polynomial surrogates and develop innovative methodologies to improve the accuracy of the polynomial surrogates. We use several numerical examples to validate the efficiency and accuracy of the proposed methodologies. Also, as demonstration on the application side, we apply the developed methodologies to a power distribution system with various uncertainty, such as power generation and consumption uncertainty. The results demonstrate that our proposed approaches substantially reduce the computational cost associated with probabilistic power flow analysis and probabilistic system control. Additionally, for the cases that data is constantly streaming from the sensors within the system, a computationally fast online predictive model is introduced, that is capable of adjusting the predictions once system faces significant disruptions. The efficiency and accuracy of the proposed approach is demonstrated using a real-world extreme scenario, namely the Woolsey wildfire in California, following which traffic patterns significantly changed. Specifically, we study traffic conditions in locations close to the wildfire and show that the proposed approach can capture and accurately predict the post-disaster changes.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-12-01"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/106419"],"dc:language":["en"],"dc:rights":["Copyright 2019 Negin Alemazkoor"],"dc:subject":["Infrastructure systems, Predictive analysis"],"dc:title":["Polynomial approximations for fast predictive analysis of infrastructure systems: Applications to power and transportation systems"],"dc:type":["text"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:47Z"}