Managing a sustainable built environment with a large number of buildings rests on the ability to assess and improve the performance of the building stock over time. Building stock models are cornerstones to the assessment of the combined impact of energy-related building interventions across different spatial and temporal scales. However, such models, particularly those accounting for both physical formulation and social behaviors of the underlying buildings, are still in their infancy. This research strives to more thoroughly examine how buildings perform aggregately in energy usage by focusing on how to tackled three major technical challenges: (1) quantifying building energy performance in an objective and scalable manner, (2) mapping building stock model space to real-world data space, and (3) quantifying and evaluating energy intervention behaviors of a building stock. This thesis hypothesizes that a new paradigm of aggregation of large-scale building stocks can lead to (1) an accurate and efficient intervention analysis model and (2) a functionally comprehensive decision support tool for building stock energy intervention analysis. Specifically, this thesis presents three methodologies. To address the first challenge, this thesis develops a normative building physical energy model that can rapidly estimate single building energy performance with respect to its design and operational characteristics. To address the second challenge, the thesis proposes a statistical procedure using regression and Markov chain Monte Carlo (MCMC) sampling techniques that inverse-estimate building parameters based on building stock energy consumption survey data. The outcomes of this statistical procedure validate the approach of using prototypical buildings for two types of intervention analysis: energy retrofit and demand response. These two cases are implemented in an agent-based modeling and simulation (ABMS) framework to tackle the third challenge. This thesis research contributes to the body of knowledge pertaining to building energy modeling beyond the single building scale. The proposed framework can be used by energy policy makers and utilities for the evaluation of energy retrofit incentives and demand-response program economics.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/43704 |
| Date | 04 April 2012 |
| Creators | Zhao, Fei |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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