Date of Award

Spring 1-1-2017

Document Type


Degree Name

Doctor of Philosophy (PhD)

First Advisor

John Zhai

Second Advisor

Moncef Krarti

Third Advisor

Wil V. Srubar

Fourth Advisor

Rajagopalan Balaji

Fifth Advisor

Kevin J. Krizek


Urban areas consume two-thirds of the world's energy and account for 71% of global greenhouse gas emissions. In the U.S., residential and commercial buildings consume 22% and 19% of the total energy use, respectively. In response to current energy and environmental issues, policymakers have been actively engaged in the establishment of regulations and incentives to promote strategies for energy and greenhouse gas reduction in urban areas. To assist such decision makings requires an accurate and dynamic prediction and analysis of urban energy needs and developing trends, especially for building stocks.

Five primary challenges exist in modeling urban level building energy uses: (a) lack of building details for massive infrastructures (e.g., building envelope, floor area, age); (b) lack of knowledge of occupant related parameters (e.g., human behaviors, equipment power density, heating and cooling temperature set points); (c) uncertainties in building energy models; (d) unavailability of energy use data for validation; (e) computational effort. To address such challenges, a stochastic-deterministic-coupled modeling approach was developed. In this method, the energy uses of probability-based representative buildings were calculated with a deterministic engineering-based tool (e.g., EnergyPlus) with probabilistic inputs (e.g., building materials, human behaviors).

Detailed analyses were performed considering the accuracy of estimation and computational time for each step of the process. The analysis of building stock information and the impact of its uncertainty were also examined. The proposed stochastic-deterministic-coupled approach was demonstrated on the campus scale. The proposed model has the following advantages over the existing building stock models: (a) Applicable to various building types; (b) Fast computational time; (c) predictability by energy end-use type; (d) Availability of various temporal and spatial; (e) Availability for retrofit analysis of building stock. The proposed model enables cost-effective energy estimation at large scale considering uncertainties.