Energy modeling optimization of net zero energy buildings under varying occupant behaviour

Net zero energy buildings have been increasing in popularity in recent years due to their positive impact on the environment. Current research indicates that net zero energy buildings have a capital cost increase of between 5% to 19% over other high-performance buildings. To reduce this cost, building simulation optimization (BSO) has emerged as a design tool. Once constructed, occupant behavior remains as an obstacle to the success of a net zero energy building. This thesis sought to understand the cost implications of varying occupant behavior and climate types when using the non-dominated sorting genetic algorithm (NSGA-II) to optimize the capital cost of a net zero energy office building. Models in Seattle, Houston and Helena were run through a BSO program for 200 generations using the NSGA-II algorithm under three varying occupant behavior assumptions: low energy, reference energy and high energy. The results of the models indicated that the NSGA-II stabilized on an optimal solution within 150 generations. When assuming reference energy occupant behavior, the results indicated that cost of achieving net zero energy varied greatly by location, primarily due to effects of the latitude on photovoltaic (PV) production. The increased costs resulting from latitude were amplified when assuming high energy occupant behavior and dampened when assuming low energy occupant behavior. These cost effects were primarily attributed to the increased number of PV panels needed to reach net zero energy, and due to the need for expensive, energy efficient technologies to be utilized for the building to reach the goal of net zero energy.