{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/80888"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/80888","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Towards Net-Zero Energy Building Clusters: Designing Operational Strategies For Optimal Energy Management","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Odonkor, Philip"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Lewis, Kemper","Mechanical and Aerospace Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-10-29T16:47:53Z","date_published":"2019-10-29T16:47:53Z","updated_at":"2026-07-27T19:05:25Z","subjects":["mechanical engineering"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/80888","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Lewis, Kemper","Mechanical and Aerospace Engineering"]},{"key":"dc:creator","label":"Author","values":["Odonkor, Philip"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-10-29T16:47:53Z","2019","2019-08-04 16:22:14"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["mechanical engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/80888"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","Traditionally viewed as mere energy consumers, buildings have in recent years evolved, capitalizing on distributed energy resources and smart grid technologies to efficiently use and generate energy. This has led to the development of a new class of building known as Net-Zero energy buildings. These high energy performance structures are capable of achieving a balanced energy budget over an annual cycle. Enabled by the intelligent design of operational strategies, this dissertation builds on the idea of Net-Zero buildings by expanding its scope to encompass communities of buildings, known as building clusters. This idea is realized by developing computational decision tools which combine design optimization and machine learning to realize energy efficiencies by exploiting emerging synergies between the smart grid, cyber–physical systems, renewable energy generation and energy storage systems. To this end, this work makes three major contributions to the state-of-the-art; (i) it develops an adaptive, decentralized decision framework for energy optimization in Net-Zero energy building clusters; (ii) it presents a novel Pareto Band approach to allow for robust operational strategies to be designed under uncertainty; and (iii) it develops a data-driven, automated approach for designing operational strategies in building clusters by learning directly from energy consumption profiles. The presented experimental results demonstrate notable energy cost performance improvements across a variety of cluster combinations studied, and an ability to scale to larger systems with minimal computational cost."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Towards Net-Zero Energy Building Clusters: Designing Operational Strategies For Optimal Energy Management"]}]}],"canonical_facts":{"dc:contributor":["Lewis, Kemper","Mechanical and Aerospace Engineering"],"dc:creator":["Odonkor, Philip"],"dc:date":["2019-10-29T16:47:53Z","2019","2019-08-04 16:22:14"],"dc:description":["Ph.D.","Traditionally viewed as mere energy consumers, buildings have in recent years evolved, capitalizing on distributed energy resources and smart grid technologies to efficiently use and generate energy. This has led to the development of a new class of building known as Net-Zero energy buildings. These high energy performance structures are capable of achieving a balanced energy budget over an annual cycle. Enabled by the intelligent design of operational strategies, this dissertation builds on the idea of Net-Zero buildings by expanding its scope to encompass communities of buildings, known as building clusters. This idea is realized by developing computational decision tools which combine design optimization and machine learning to realize energy efficiencies by exploiting emerging synergies between the smart grid, cyber–physical systems, renewable energy generation and energy storage systems. To this end, this work makes three major contributions to the state-of-the-art; (i) it develops an adaptive, decentralized decision framework for energy optimization in Net-Zero energy building clusters; (ii) it presents a novel Pareto Band approach to allow for robust operational strategies to be designed under uncertainty; and (iii) it develops a data-driven, automated approach for designing operational strategies in building clusters by learning directly from energy consumption profiles. The presented experimental results demonstrate notable energy cost performance improvements across a variety of cluster combinations studied, and an ability to scale to larger systems with minimal computational cost."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/80888"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["mechanical engineering"],"dc:title":["Towards Net-Zero Energy Building Clusters: Designing Operational Strategies For Optimal Energy Management"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:25Z"}