{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/2009"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/2009","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Energy load prediction for Open Charge Point Protocol - compliant electric vehicle charging stations","abstract":"This thesis develops an Open Charge Point Protocol (OCPP)–compliant Charging Station Management System (CSMS) and end-to-end data pipeline to enable short-term energy forecasting for individual EV charging stations. The system captures and processes OCPP transactions in PostgreSQL, deriving per-session energy for consistent forecasting targets. Using this platform, four models—Random Forest, K-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), and Prophet—are evaluated with expanding-window cross-validation on 30 stations and Adaptive Charging Network (ACN) datasets. Key findings show that data regularity and density strongly influence accuracy: high usage, structured sites achieve higher R2 values, while sparse, irregular stations remain challenging. Aggregating to 2–4 hour intervals improves R2 at busy sites but increases absolute errors elsewhere. The study contributes (i) an operational OCPP-compliant CSMS, (ii) a method for transforming telemetry into forecasting targets, and (iii) comparative guidance on model selection and time resolution for operational energy management.","abstract_html":"This thesis develops an Open Charge Point Protocol (OCPP)–compliant Charging Station Management System (CSMS) and end-to-end data pipeline to enable short-term energy forecasting for individual EV charging stations. The system captures and processes OCPP transactions in PostgreSQL, deriving per-session energy for consistent forecasting targets. Using this platform, four models—Random Forest, K-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), and Prophet—are evaluated with expanding-window cross-validation on 30 stations and Adaptive Charging Network (ACN) datasets. Key findings show that data regularity and density strongly influence accuracy: high usage, structured sites achieve higher R2 values, while sparse, irregular stations remain challenging. Aggregating to 2–4 hour intervals improves R2 at busy sites but increases absolute errors elsewhere. The study contributes (i) an operational OCPP-compliant CSMS, (ii) a method for transforming telemetry into forecasting targets, and (iii) comparative guidance on model selection and time resolution for operational energy management.","abstract_has_math":false,"creators":["Lee, Hyon"],"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":["Azim, Akramul"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05-01","date_published":"2025-05-01","updated_at":"2026-07-24T05:35:32Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/2009","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Azim, Akramul"]},{"key":"dc:creator","label":"Author","values":["Lee, Hyon"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-09-19T19:58:33Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-09-19T19:58:33Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-05-01"]},{"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/10155/2009"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis develops an Open Charge Point Protocol (OCPP)–compliant Charging Station Management System (CSMS) and end-to-end data pipeline to enable short-term energy forecasting for individual EV charging stations. The system captures and processes OCPP transactions in PostgreSQL, deriving per-session energy for consistent forecasting targets. Using this platform, four models—Random Forest, K-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), and Prophet—are evaluated with expanding-window cross-validation on 30 stations and Adaptive Charging Network (ACN) datasets. Key findings show that data regularity and density strongly influence accuracy: high usage, structured sites achieve higher R2 values, while sparse, irregular stations remain challenging. Aggregating to 2–4 hour intervals improves R2 at busy sites but increases absolute errors elsewhere. The study contributes (i) an operational OCPP-compliant CSMS, (ii) a method for transforming telemetry into forecasting targets, and (iii) comparative guidance on model selection and time resolution for operational energy management."]},{"key":"dc:title","label":"Title","values":["Energy load prediction for Open Charge Point Protocol - compliant electric vehicle charging stations"]}]}],"canonical_facts":{"dc:contributor.advisor":["Azim, Akramul"],"dc:creator":["Lee, Hyon"],"dc:date.accessioned":["2025-09-19T19:58:33Z"],"dc:date.available":["2025-09-19T19:58:33Z"],"dc:date.issued":["2025-05-01"],"dc:description.abstract":["This thesis develops an Open Charge Point Protocol (OCPP)–compliant Charging Station Management System (CSMS) and end-to-end data pipeline to enable short-term energy forecasting for individual EV charging stations. The system captures and processes OCPP transactions in PostgreSQL, deriving per-session energy for consistent forecasting targets. Using this platform, four models—Random Forest, K-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), and Prophet—are evaluated with expanding-window cross-validation on 30 stations and Adaptive Charging Network (ACN) datasets. Key findings show that data regularity and density strongly influence accuracy: high usage, structured sites achieve higher R2 values, while sparse, irregular stations remain challenging. Aggregating to 2–4 hour intervals improves R2 at busy sites but increases absolute errors elsewhere. The study contributes (i) an operational OCPP-compliant CSMS, (ii) a method for transforming telemetry into forecasting targets, and (iii) comparative guidance on model selection and time resolution for operational energy management."],"dc:identifier.uri":["https://hdl.handle.net/10155/2009"],"dc:language.iso":["en"],"dc:title":["Energy load prediction for Open Charge Point Protocol - compliant electric vehicle charging stations"],"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":"2026-07-24T05:35:32Z"}