Model-Based Systems Engineering Approach to Energy Efficient Building Design
Daily, David R
Date: November 01 - November 01, 2012
During my senior year at the University of Maryland, I was challenged to develop and investigate a research question utilizing Systems Engineering methodologies and tools for my capstone project. My project uses Model-Based Systems Engineering to model and simulate a residential building’s HVAC operational effect and perform trade-off analysis on the building parameters. I have since continued this research for my Masters of Science in Systems Engineering thesis.
According to the U.S. Energy Information Administration’s “Annual Energy Outlook 2012”, the residential and commercial sectors use a combined 28 percent of total U.S. energy consumption. With energy efficiency technology improvements and policy change, the EIA estimates that building energy consumption could be reduced by 10%-18.5% within these sectors by 2035. Reducing building energy demand is a necessary step in curbing the GHG issue and creates new requirements to meet these goals. Model-Based System Engineering allows for easy requirements verification and validation while creating a truly optimized system.
I used SysML to develop a model of the building, its components, and other inputs into the system (i.e. weather). I am able to create a robust model in the SysML program MagicDraw, including detailed parametric, activity, and sequence diagrams, along with significant requirements definitions. I made a basic thermodynamics simulation in MATLAB and linked the two programs. With this link established, I automated the parametric analysis of the building parameters. The resulting 846 iterations gave me performance data to perform trade-off analysis to optimize cost and energy efficiency.
When the setpoint temperature of the building is outside the allowable range, the HVAC system uses energy to condition the space, while thermal losses and gains vary the indoor temperature. The efficiency metric is defined as the period length between the activations of the HVAC system. A longer period indicates a more energy efficient system. Through trade-off analysis, I conclude that the parameter of insulation R-Value had the greatest cost-to-efficiency benefit while window U-Value had the second most impact.