Incorporating Observed Data into Early Design Energy Models for Life Cycle Cost and Emissions Analysis of Campus Buildings

Abstract As energy prices increase along with a societal desire for reducing the impact of global warming, it is common for campus decision-makers to consider the life cycle and carbon emissions of future and existing buildings. This work proposes a technique for leveraging observed energy and weather data to represent internal loads for heating, cooling, and occupancy of different building types to simplify the analysis of multiple buildings. Template energy profiles and modular energy models simulate the operation and energy usage of buildings with different HVAC equipment types. This approach makes it possible to evaluate multiple buildings from a broader campus-level perspective. This work proposes a discrete multi-objective optimization framework to select the optimal HVAC system for each building to minimize life cycle cost and emissions. A case study for the University of Utah campus demonstrates the application of the proposed framework to consider a group of new buildings with different principal building activity and size with five different HVAC systems. Results show it is possible to reduce overall carbon emissions of the buildings by 15% while only increasing the life cycle cost by 2.4% by considering a combination of systems rather than a single HVAC system type.