{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108248"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108248","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Data-driven models to evaluate root causes of energy performance gaps in office buildings","abstract":"Designers develop high-performance office building designs with efficient envelopes, and Heating, Ventilation and Air Conditioning (HVAC) systems utilizing energy modeling tools with forecasted occupancy, plug load, and operational profiles as inputs. Their expected performance is often not met due to discrepancies between modeled and actual energy behaviors resulting in energy performance gaps. Energy performance gaps stemming out of design, construction and operation phases, pose serious challenges to the credibility of the design and engineering sectors in achieving predicted energy goals of the project. Data-driven models, based on the actual building energy consumption data, offer an excellent means to evaluate significant root-causes of energy performance gaps; occupancy, envelope and HVAC operations. On the one hand, this supports the feedback loop to the design process from real-time building energy performance to predict energy performance accurately, choose optimal design solutions and, mitigate construction quality management issues in future designs. On the other hand, this provides perfect feedback to optimize energy performance in existing buildings. This doctoral research presents data-driven modeling methodologies leveraging energy consumption data, advanced statistical methods, and expert domain knowledge, to assess the occupancy profiles, envelope thermal response, and HVAC systems’ performance, in office buildings. The study proposes data-driven techniques to generate evidence-based knowledge to designers about the real-time energy performance of their design decisions and required inputs for energy simulation models, that support the development of high-performance office designs in the future with minimal energy performance gaps.","abstract_html":"Designers develop high-performance office building designs with efficient envelopes, and Heating, Ventilation and Air Conditioning (HVAC) systems utilizing energy modeling tools with forecasted occupancy, plug load, and operational profiles as inputs. Their expected performance is often not met due to discrepancies between modeled and actual energy behaviors resulting in energy performance gaps. Energy performance gaps stemming out of design, construction and operation phases, pose serious challenges to the credibility of the design and engineering sectors in achieving predicted energy goals of the project. Data-driven models, based on the actual building energy consumption data, offer an excellent means to evaluate significant root-causes of energy performance gaps; occupancy, envelope and HVAC operations. On the one hand, this supports the feedback loop to the design process from real-time building energy performance to predict energy performance accurately, choose optimal design solutions and, mitigate construction quality management issues in future designs. On the other hand, this provides perfect feedback to optimize energy performance in existing buildings. This doctoral research presents data-driven modeling methodologies leveraging energy consumption data, advanced statistical methods, and expert domain knowledge, to assess the occupancy profiles, envelope thermal response, and HVAC systems’ performance, in office buildings. The study proposes data-driven techniques to generate evidence-based knowledge to designers about the real-time energy performance of their design decisions and required inputs for energy simulation models, that support the development of high-performance office designs in the future with minimal energy performance gaps.","abstract_has_math":false,"creators":["Khamma, Thulasi Ram"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Architecture","degree_department":null,"school":null,"contributors":["Boubekri, Mohamed","Strand, Richard K","Guerrier, Stéphane","Narisetty, Naveen Naidu"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-27T00:49:55Z","date_published":"2020-08-27T00:49:55Z","updated_at":"2026-07-22T22:24:48Z","subjects":["Building Energy Performance Gaps, High-Performance Office Buildings, Building Energy Efficiency"],"languages":["en"],"rights":["Copyright 2020 Thulasi Ram Khamma"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108248","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Boubekri, Mohamed","Strand, Richard K","Guerrier, Stéphane","Narisetty, Naveen Naidu"]},{"key":"dc:creator","label":"Author","values":["Khamma, Thulasi Ram"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-27T00:49:55Z","2022-08-27T00:51:40Z","2020-04-15","2020-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Architecture"]},{"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":["Building Energy Performance Gaps, High-Performance Office Buildings, Building Energy Efficiency"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Thulasi Ram Khamma"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108248"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Designers develop high-performance office building designs with efficient envelopes, and Heating, Ventilation and Air Conditioning (HVAC) systems utilizing energy modeling tools with forecasted occupancy, plug load, and operational profiles as inputs. Their expected performance is often not met due to discrepancies between modeled and actual energy behaviors resulting in energy performance gaps. Energy performance gaps stemming out of design, construction and operation phases, pose serious challenges to the credibility of the design and engineering sectors in achieving predicted energy goals of the project. Data-driven models, based on the actual building energy consumption data, offer an excellent means to evaluate significant root-causes of energy performance gaps; occupancy, envelope and HVAC operations. On the one hand, this supports the feedback loop to the design process from real-time building energy performance to predict energy performance accurately, choose optimal design solutions and, mitigate construction quality management issues in future designs. On the other hand, this provides perfect feedback to optimize energy performance in existing buildings. This doctoral research presents data-driven modeling methodologies leveraging energy consumption data, advanced statistical methods, and expert domain knowledge, to assess the occupancy profiles, envelope thermal response, and HVAC systems’ performance, in office buildings. The study proposes data-driven techniques to generate evidence-based knowledge to designers about the real-time energy performance of their design decisions and required inputs for energy simulation models, that support the development of high-performance office designs in the future with minimal energy performance gaps.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-05-01","The student, Thulasi Ram Khamma, accepted the attached license on 2020-04-10 at 22:26.","The student, Thulasi Ram Khamma, submitted this Dissertation for approval on 2020-04-10 at 22:56.","This Dissertation was approved for publication on 2020-04-15 at 11:37.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14960 on 2020-08-25 at 17:40:05","Made available in DSpace on 2020-08-27T00:49:55Z (GMT). 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Their expected performance is often not met due to discrepancies between modeled and actual energy behaviors resulting in energy performance gaps. Energy performance gaps stemming out of design, construction and operation phases, pose serious challenges to the credibility of the design and engineering sectors in achieving predicted energy goals of the project. Data-driven models, based on the actual building energy consumption data, offer an excellent means to evaluate significant root-causes of energy performance gaps; occupancy, envelope and HVAC operations. On the one hand, this supports the feedback loop to the design process from real-time building energy performance to predict energy performance accurately, choose optimal design solutions and, mitigate construction quality management issues in future designs. On the other hand, this provides perfect feedback to optimize energy performance in existing buildings. This doctoral research presents data-driven modeling methodologies leveraging energy consumption data, advanced statistical methods, and expert domain knowledge, to assess the occupancy profiles, envelope thermal response, and HVAC systems’ performance, in office buildings. The study proposes data-driven techniques to generate evidence-based knowledge to designers about the real-time energy performance of their design decisions and required inputs for energy simulation models, that support the development of high-performance office designs in the future with minimal energy performance gaps.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-05-01","The student, Thulasi Ram Khamma, accepted the attached license on 2020-04-10 at 22:26.","The student, Thulasi Ram Khamma, submitted this Dissertation for approval on 2020-04-10 at 22:56.","This Dissertation was approved for publication on 2020-04-15 at 11:37.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14960 on 2020-08-25 at 17:40:05","Made available in DSpace on 2020-08-27T00:49:55Z (GMT). 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