Most building automation systems operate with settings based on design assumptions with fixed operational schedules and fixed occupancy, when in fact both schedules and occupancy levels vary dynamically. In particular, the heating ventilation and air conditioning (HVAC) system provides a minimum ventilation airflow calculated for the maximum room capacity, when rooms are rarely fully occupied. Energy is wasted by over-supplying and conditioning air that is not required, which also leads to thermal discomfort. In higher educational institutions, where classroom occupancy goals vary from 60% to 80% of their maximum capacity, potential savings are substantial. Existing occupancy and schedule information from academic registration can be integrated with the facility data and the building automation system, allowing dynamic resetting of the controllers. This dissertation provides a methodology to reduce HVAC energy consumption by using occupancy information from the academic registrar. The methodology integrates three energy conservation strategies: shortening schedules, modifying thermostat settings and reducing the minimum airflow. Analysis of the proposed solution includes an economic benefit estimation at a campus level with validation through an experimental study performed on a LEED platinum building. Experiment results achieved an electricity savings of 39% and a natural gas savings of 31% for classrooms’ air conditioning consumption. Extending these savings to the campus level yields 164 MWh of electricity savings per year, 48MMBtu natural gas savings per year, 35.16 MTCO2 of greenhouse gases emissions reduction per year, approximately $20k economic savings per year.
Department, Program, or Center
Thomas A. Trabold
Marozas-Aliaga, Lourdes, "Data Driven Energy Efficiency Strategies for Commercial Buildings Using Occupancy Information" (2018). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus