{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1931"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1931","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"AI-driven optimization framework with domain-specific Large Language Models for renewable energy and hydrogen deployment","abstract":"This thesis introduces a novel framework for achieving zero-net emissions through Hybrid Renewable Energy Systems (HRES). It presents RE-LLaMA, a domain-specific Large Language Model trained on renewable and hydrogen energy data. The framework integrates RE-LLaMA with MG-OPT, a multi-objective optimization system that employs Mixed-integer linear Programming and Pareto-based optimization techniques. The framework uniquely combines photovoltaic, wind, and fuel cell sources with hydrogen production and storage capabilities while simultaneously optimizing four key objectives: minimizing system costs and emissions while maximizing renewable energy utilization and surplus profit through transactive energy mechanisms. Through comprehensive validation across three scenarios (30-50 kW systems), including EV and hydrogen fuel cell vehicle integration, the framework demonstrates remarkable environmental and economic benefits, achieving up to 95% emissions reduction and generating daily profits of $31-$45 through surplus electricity trading. The RE-LLaMA system's effectiveness is validated through LLM-as-Judge and human evaluation methodologies, with RE-LLaMA significantly outperforming the base model in renewable and hydrogen energy tasks, while MG-OPT's sophisticated algorithms enable precise resource allocation in grid-connected operation, ultimately advancing the field by bridging the gap between theoretical multi-objective optimization and practical implementation requirements for sustainable energy systems.","abstract_html":"This thesis introduces a novel framework for achieving zero-net emissions through Hybrid Renewable Energy Systems (HRES). It presents RE-LLaMA, a domain-specific Large Language Model trained on renewable and hydrogen energy data. The framework integrates RE-LLaMA with MG-OPT, a multi-objective optimization system that employs Mixed-integer linear Programming and Pareto-based optimization techniques. The framework uniquely combines photovoltaic, wind, and fuel cell sources with hydrogen production and storage capabilities while simultaneously optimizing four key objectives: minimizing system costs and emissions while maximizing renewable energy utilization and surplus profit through transactive energy mechanisms. Through comprehensive validation across three scenarios (30-50 kW systems), including EV and hydrogen fuel cell vehicle integration, the framework demonstrates remarkable environmental and economic benefits, achieving up to 95% emissions reduction and generating daily profits of $31-$45 through surplus electricity trading. The RE-LLaMA system's effectiveness is validated through LLM-as-Judge and human evaluation methodologies, with RE-LLaMA significantly outperforming the base model in renewable and hydrogen energy tasks, while MG-OPT's sophisticated algorithms enable precise resource allocation in grid-connected operation, ultimately advancing the field by bridging the gap between theoretical multi-objective optimization and practical implementation requirements for sustainable energy systems.","abstract_has_math":true,"creators":["Hemied, Omar S."],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Software Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Gaber, Hossam"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-01","date_published":"2025-04-01","updated_at":"2026-07-24T05:35:36Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1931","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Gaber, Hossam"]},{"key":"dc:creator","label":"Author","values":["Hemied, Omar S."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-04-29T17:36:30Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-04-29T17:36:30Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-04-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Software 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/10155/1931"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis introduces a novel framework for achieving zero-net emissions through Hybrid Renewable Energy Systems (HRES). It presents RE-LLaMA, a domain-specific Large Language Model trained on renewable and hydrogen energy data. The framework integrates RE-LLaMA with MG-OPT, a multi-objective optimization system that employs Mixed-integer linear Programming and Pareto-based optimization techniques. The framework uniquely combines photovoltaic, wind, and fuel cell sources with hydrogen production and storage capabilities while simultaneously optimizing four key objectives: minimizing system costs and emissions while maximizing renewable energy utilization and surplus profit through transactive energy mechanisms. Through comprehensive validation across three scenarios (30-50 kW systems), including EV and hydrogen fuel cell vehicle integration, the framework demonstrates remarkable environmental and economic benefits, achieving up to 95% emissions reduction and generating daily profits of $31-$45 through surplus electricity trading. The RE-LLaMA system's effectiveness is validated through LLM-as-Judge and human evaluation methodologies, with RE-LLaMA significantly outperforming the base model in renewable and hydrogen energy tasks, while MG-OPT's sophisticated algorithms enable precise resource allocation in grid-connected operation, ultimately advancing the field by bridging the gap between theoretical multi-objective optimization and practical implementation requirements for sustainable energy systems."]},{"key":"dc:title","label":"Title","values":["AI-driven optimization framework with domain-specific Large Language Models for renewable energy and hydrogen deployment"]}]}],"canonical_facts":{"dc:contributor.advisor":["Gaber, Hossam"],"dc:creator":["Hemied, Omar S."],"dc:date.accessioned":["2025-04-29T17:36:30Z"],"dc:date.available":["2025-04-29T17:36:30Z"],"dc:date.issued":["2025-04-01"],"dc:description.abstract":["This thesis introduces a novel framework for achieving zero-net emissions through Hybrid Renewable Energy Systems (HRES). It presents RE-LLaMA, a domain-specific Large Language Model trained on renewable and hydrogen energy data. The framework integrates RE-LLaMA with MG-OPT, a multi-objective optimization system that employs Mixed-integer linear Programming and Pareto-based optimization techniques. The framework uniquely combines photovoltaic, wind, and fuel cell sources with hydrogen production and storage capabilities while simultaneously optimizing four key objectives: minimizing system costs and emissions while maximizing renewable energy utilization and surplus profit through transactive energy mechanisms. Through comprehensive validation across three scenarios (30-50 kW systems), including EV and hydrogen fuel cell vehicle integration, the framework demonstrates remarkable environmental and economic benefits, achieving up to 95% emissions reduction and generating daily profits of $31-$45 through surplus electricity trading. The RE-LLaMA system's effectiveness is validated through LLM-as-Judge and human evaluation methodologies, with RE-LLaMA significantly outperforming the base model in renewable and hydrogen energy tasks, while MG-OPT's sophisticated algorithms enable precise resource allocation in grid-connected operation, ultimately advancing the field by bridging the gap between theoretical multi-objective optimization and practical implementation requirements for sustainable energy systems."],"dc:identifier.uri":["https://hdl.handle.net/10155/1931"],"dc:language.iso":["en"],"dc:title":["AI-driven optimization framework with domain-specific Large Language Models for renewable energy and hydrogen deployment"],"dc:type":["Thesis"],"thesis:degree_discipline":["Software Engineering"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:36Z"}