Retrofitting of existing buildings is essential to reach reduction targets in energy consumption and greenhouse gas emission. In the current practice of a retrofit decision process, professionals perform energy audits, and construct dynamic simulation models to benchmark the performance of existing buildings and predict the effect of retrofit interventions. In order to enhance the reliability of simulation models, they typically calibrate simulation models based on monitored energy use data. The calibration techniques used for this purpose are manual and expert-driven. The current practice has major drawbacks: (1) the modeling and calibration methods do not scale to large portfolio of buildings due to their high costs and heavy reliance on expertise, and (2) the resulting deterministic models do not provide insight into underperforming risks associated with each retrofit intervention. This thesis has developed a new retrofit analysis framework that is suitable for large-scale analysis and risk-conscious decision-making. The framework is based on the use of normative models and Bayesian calibration techniques. Normative models are light-weight quasi-steady state energy models that can scale up to large sets of buildings, i.e. to city and regional scale. In addition, they do not require modeling expertise since they follow a set of modeling rules that produce a standard measure for energy performance. The normative models are calibrated under a Bayesian approach such that the resulting calibrated models quantify uncertainties in the energy outcomes of a building. Bayesian calibration models can also incorporate additional uncertainties associated with retrofit interventions to generate probability distributions of retrofit performance. Probabilistic outputs can be straightforwardly translated into a measure that quantifies underperforming risks of retrofit interventions and thus enable decision making relative to the decision-makers' rational objectives and risk attitude. This thesis demonstrates the feasibility of the new framework on retrofit applications by verifying the following two hypotheses: (1) normative models supported by Bayesian calibration have sufficient model fidelity to adequately support retrofit decisions, and (2) they can support risk-conscious decision-making by explicitly quantifying risks associated with retrofit options. The first and second hypotheses are examined through case studies that compare outcomes from the calibrated normative model with those from a similarly calibrated transient simulation model and compare decisions derived by the proposed framework with those derived by standard practices respectively. The new framework will enable cost-effective retrofit analysis at urban scale with explicit management of uncertainties.

The research objective of this thesis is to formulate and demonstrate a computational framework for modeling the design decisions of engineers. This framework is intended to be descriptive in nature as opposed to prescriptive or normative; the output of the model represents a plausible result of a designer's decision making process. The framework decomposes the decision into three elements: the problem statement, the designer's beliefs about the alternatives, and the designer's preferences. Multi-attribute utility theory is used to capture designer preferences for multiple objectives under uncertainty. Machine-learning techniques are used to store the designer's knowledge and to make Bayesian inferences regarding the attributes of alternatives. These models are integrated into the framework of a Markov decision process to simulate multiple sequential decisions. The overall framework enables the designer's decision problem to be transformed into an optimization problem statement; the simulated designer selects the alternative with the maximum expected utility. Although utility theory is typically viewed as a normative decision framework, the perspective in this research is that the approach can be used in a descriptive context for modeling rational and non-time critical decisions by engineering designers. This approach is intended to enable the formalisms of utility theory to be used to design human subjects experiments involving engineers in design organizations based on pairwise lotteries and other methods for preference elicitation. The results of these experiments would substantiate the selection of parameters in the model to enable it to be used to diagnose potential problems in engineering design projects. The purpose of the decision-making framework is to enable the development of a design process simulation of an organization involved in the development of a large-scale complex engineered system such as an aircraft or spacecraft. The decision model will allow researchers to determine the broader effects of individual engineering decisions on the aggregate dynamics of the design process and the resulting performance of the designed artifact itself. To illustrate the model's applicability in this context, the framework is demonstrated on three example problems: a one-dimensional decision problem, a multidimensional turbojet design problem, and a variable fidelity analysis problem. Individual utility functions are developed for designers in a requirements-driven design problem and then combined into a multi-attribute utility function. Gaussian process models are used to represent the designer's beliefs about the alternatives, and a custom covariance function is formulated to more accurately represent a designer's uncertainty in beliefs about the design attributes.