{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/2041"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/2041","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Modeling and prediction of residential service transformer’s demand considering high penetration of electric vehicles","abstract":"The rapid growth of electric vehicles adoption has introduced new challenges for distribution transformer sizing and thermal aging assessment. Traditional sizing methods based on peak demand and diversity factors fail to capture the dynamic, time-varying nature of residential loads influenced by electric vehicles charging. In this thesis, a data-driven model is developed to predict the transformer’s demand when considering large penetration of electric vehicles, which is then used to estimate the transformer’s economic capacity. The proposed model enables more accurate, economical, and computationally efficient transformer sizing while providing actionable insights to mitigate premature aging risks. The development of such a model will enable safe integration of large penetration of electric vehicles in the residential sector while ensuring economic sizing of transformers and, hence deferring premature replacement.","abstract_html":"The rapid growth of electric vehicles adoption has introduced new challenges for distribution transformer sizing and thermal aging assessment. Traditional sizing methods based on peak demand and diversity factors fail to capture the dynamic, time-varying nature of residential loads influenced by electric vehicles charging. In this thesis, a data-driven model is developed to predict the transformer’s demand when considering large penetration of electric vehicles, which is then used to estimate the transformer’s economic capacity. The proposed model enables more accurate, economical, and computationally efficient transformer sizing while providing actionable insights to mitigate premature aging risks. The development of such a model will enable safe integration of large penetration of electric vehicles in the residential sector while ensuring economic sizing of transformers and, hence deferring premature replacement.","abstract_has_math":false,"creators":["Aziz, Muhammad Hamdan"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Ibrahim, Walid Morsi"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12-01","date_published":"2025-12-01","updated_at":"2026-07-24T05:35:41Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/2041","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Ibrahim, Walid Morsi"]},{"key":"dc:creator","label":"Author","values":["Aziz, Muhammad Hamdan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-20T15:20:15Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-12-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/2041"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The rapid growth of electric vehicles adoption has introduced new challenges for distribution transformer sizing and thermal aging assessment. Traditional sizing methods based on peak demand and diversity factors fail to capture the dynamic, time-varying nature of residential loads influenced by electric vehicles charging. In this thesis, a data-driven model is developed to predict the transformer’s demand when considering large penetration of electric vehicles, which is then used to estimate the transformer’s economic capacity. The proposed model enables more accurate, economical, and computationally efficient transformer sizing while providing actionable insights to mitigate premature aging risks. The development of such a model will enable safe integration of large penetration of electric vehicles in the residential sector while ensuring economic sizing of transformers and, hence deferring premature replacement."]},{"key":"dc:title","label":"Title","values":["Modeling and prediction of residential service transformer’s demand considering high penetration of electric vehicles"]}]}],"canonical_facts":{"dc:contributor.advisor":["Ibrahim, Walid Morsi"],"dc:creator":["Aziz, Muhammad Hamdan"],"dc:date.accessioned":["2026-01-20T15:20:15Z"],"dc:date.issued":["2025-12-01"],"dc:description.abstract":["The rapid growth of electric vehicles adoption has introduced new challenges for distribution transformer sizing and thermal aging assessment. Traditional sizing methods based on peak demand and diversity factors fail to capture the dynamic, time-varying nature of residential loads influenced by electric vehicles charging. In this thesis, a data-driven model is developed to predict the transformer’s demand when considering large penetration of electric vehicles, which is then used to estimate the transformer’s economic capacity. The proposed model enables more accurate, economical, and computationally efficient transformer sizing while providing actionable insights to mitigate premature aging risks. The development of such a model will enable safe integration of large penetration of electric vehicles in the residential sector while ensuring economic sizing of transformers and, hence deferring premature replacement."],"dc:identifier.uri":["https://hdl.handle.net/10155/2041"],"dc:language.iso":["en"],"dc:title":["Modeling and prediction of residential service transformer’s demand considering high penetration of electric vehicles"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:41Z"}