Buildings are amongst the highest energy consumers relative to industry and transportation. They account for 40% of the world’s energy consumption, due to the need for lighting, equipment, heating, cooling and ventilation. Academic buildings are multi-purpose buildings that create a challenge on energy reduction. Most are old and have fixed occupancy schedules, resulting in high energy consumption because these buildings experience significant occupancy variation throughout the day. Five academic buildings were analyzed; their building information, energy consumption data and methods to project energy savings have been analyzed. The case studies presented different strategies on predicting energy savings, but these have been deduced to their commonalities: the black box, white box and grey box models. The black box is a data driven approach, the white box is a physics based approach and the grey box is a hybrid between the black box and the white box. Control strategies include the usage of occupancy sensors to ensure building energy usage is directly proportional to building occupancy density and that the energy is not wasted on an empty building. An application approach to University of San Francisco was also developed. The active energy retrofits for University of San Francisco have been mentioned and explored by following the black box, white box and grey box model methodology. Findings from the case studies discovered that occupant behavior can be a barrier to energy reduction as occupants are driven by maintaining personal comfort and are usually detached to energy usage consequences. For this matter awareness campaigns such as surveys and educational campaigns need to be implemented to help achieve higher building efficiency and thus lower energy consumption. If all academic buildings in the United States committed to a 5% energy reduction, then over 2 billion kWh could be saved annually.
","abstract_html":"<p>Buildings are amongst the highest energy consumers relative to industry and transportation. They account for 40% of the world’s energy consumption, due to the need for lighting, equipment, heating, cooling and ventilation. Academic buildings are multi-purpose buildings that create a challenge on energy reduction. Most are old and have fixed occupancy schedules, resulting in high energy consumption because these buildings experience significant occupancy variation throughout the day. Five academic buildings were analyzed; their building information, energy consumption data and methods to project energy savings have been analyzed. The case studies presented different strategies on predicting energy savings, but these have been deduced to their commonalities: the black box, white box and grey box models. The black box is a data driven approach, the white box is a physics based approach and the grey box is a hybrid between the black box and the white box. Control strategies include the usage of occupancy sensors to ensure building energy usage is directly proportional to building occupancy density and that the energy is not wasted on an empty building. An application approach to University of San Francisco was also developed. The active energy retrofits for University of San Francisco have been mentioned and explored by following the black box, white box and grey box model methodology. Findings from the case studies discovered that occupant behavior can be a barrier to energy reduction as occupants are driven by maintaining personal comfort and are usually detached to energy usage consequences. For this matter awareness campaigns such as surveys and educational campaigns need to be implemented to help achieve higher building efficiency and thus lower energy consumption. If all academic buildings in the United States committed to a 5% energy reduction, then over 2 billion kWh could be saved annually. </p>","abstract_has_math":false,"creators":["Duong, Paloma R"],"institution":null,"degree_name":"Master of Science in Environmental Management (MSEM)","degree_level":"Project/Capstone - Global access","degree_discipline":"Environmental Management","degree_department":null,"school":null,"contributors":["Allison Luengen"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-05-20T07:00:00Z","date_published":"2016-05-20T07:00:00Z","updated_at":"2026-07-24T05:43:31Z","subjects":["energy reduction","academic buildings","energy efficiency","occupancy sensors","occupancy profiles","building automation system","Controls and Control Theory","Energy Systems","Oil, Gas, and Energy","Sustainability"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://repository.usfca.edu/capstone/337","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Allison Luengen"]},{"key":"dc:creator","label":"Author","values":["Duong, Paloma R"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-05-18T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Environmental Management"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Project/Capstone - Global access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Environmental Management (MSEM)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["energy reduction","academic buildings","energy efficiency","occupancy sensors","occupancy profiles","building automation system","Controls and Control Theory","Energy Systems","Oil, Gas, and Energy","Sustainability"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://repository.usfca.edu/capstone/337"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Buildings are amongst the highest energy consumers relative to industry and transportation. They account for 40% of the world’s energy consumption, due to the need for lighting, equipment, heating, cooling and ventilation. Academic buildings are multi-purpose buildings that create a challenge on energy reduction. Most are old and have fixed occupancy schedules, resulting in high energy consumption because these buildings experience significant occupancy variation throughout the day. Five academic buildings were analyzed; their building information, energy consumption data and methods to project energy savings have been analyzed. The case studies presented different strategies on predicting energy savings, but these have been deduced to their commonalities: the black box, white box and grey box models. The black box is a data driven approach, the white box is a physics based approach and the grey box is a hybrid between the black box and the white box. Control strategies include the usage of occupancy sensors to ensure building energy usage is directly proportional to building occupancy density and that the energy is not wasted on an empty building. An application approach to University of San Francisco was also developed. The active energy retrofits for University of San Francisco have been mentioned and explored by following the black box, white box and grey box model methodology. Findings from the case studies discovered that occupant behavior can be a barrier to energy reduction as occupants are driven by maintaining personal comfort and are usually detached to energy usage consequences. For this matter awareness campaigns such as surveys and educational campaigns need to be implemented to help achieve higher building efficiency and thus lower energy consumption. If all academic buildings in the United States committed to a 5% energy reduction, then over 2 billion kWh could be saved annually.
"]},{"key":"dc:title","label":"Title","values":["How can occupancy modeling and occupancy sensors reduce energy usage in academic buildings: An application approach to University of San Francisco"]}]}],"canonical_facts":{"dc:contributor":["Allison Luengen"],"dc:creator":["Duong, Paloma R"],"dc:date.available":["2016-05-18T07:00:00Z"],"dc:description.abstract":["Buildings are amongst the highest energy consumers relative to industry and transportation. They account for 40% of the world’s energy consumption, due to the need for lighting, equipment, heating, cooling and ventilation. Academic buildings are multi-purpose buildings that create a challenge on energy reduction. Most are old and have fixed occupancy schedules, resulting in high energy consumption because these buildings experience significant occupancy variation throughout the day. Five academic buildings were analyzed; their building information, energy consumption data and methods to project energy savings have been analyzed. The case studies presented different strategies on predicting energy savings, but these have been deduced to their commonalities: the black box, white box and grey box models. The black box is a data driven approach, the white box is a physics based approach and the grey box is a hybrid between the black box and the white box. Control strategies include the usage of occupancy sensors to ensure building energy usage is directly proportional to building occupancy density and that the energy is not wasted on an empty building. An application approach to University of San Francisco was also developed. The active energy retrofits for University of San Francisco have been mentioned and explored by following the black box, white box and grey box model methodology. Findings from the case studies discovered that occupant behavior can be a barrier to energy reduction as occupants are driven by maintaining personal comfort and are usually detached to energy usage consequences. For this matter awareness campaigns such as surveys and educational campaigns need to be implemented to help achieve higher building efficiency and thus lower energy consumption. If all academic buildings in the United States committed to a 5% energy reduction, then over 2 billion kWh could be saved annually.
"],"dc:identifier":["https://repository.usfca.edu/capstone/337"],"dc:subject":["energy reduction","academic buildings","energy efficiency","occupancy sensors","occupancy profiles","building automation system","Controls and Control Theory","Energy Systems","Oil, Gas, and Energy","Sustainability"],"dc:title":["How can occupancy modeling and occupancy sensors reduce energy usage in academic buildings: An application approach to University of San Francisco"],"thesis:degree_discipline":["Environmental Management"],"thesis:degree_level":["Project/Capstone - Global access"],"thesis:degree_name":["Master of Science in Environmental Management (MSEM)"]},"updated_at":"2026-07-24T05:43:31Z"}