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Date of Award


Document Type

Master's report

Degree Name

Master of Science in Mechanical Engineering (MS)

College, School or Department Name

Department of Mechanical Engineering-Engineering Mechanics

First Advisor

Mahdi Shahbakhti


Rush D. Robinett III


Model-based control of building energy offers an attractive way to minimize energy consumption in buildings. Model-based controllers require mathematical models that can accurately predict the behavior of the system. For buildings, specifically, these models are difficult to obtain due to time varying, and nonlinear nature of building dynamics. Also, model-based controllers often need information of all states, although not all the states of a building model are measurable. In this PhD proposal, I propose a modeling framework for “on-line estimation" of states and unknown parameters of buildings, leading to the Parameter-Adaptive Building (PAB) model. The results indicate that the new framework can accurately predict states and parameters of the building thermal model. Model uncertainty is unavoidable for building HVAC systems. In this PhD proposal, the impact of model uncertainty is characterized on model-based controllers, e.g. model predictive control (MPC), and robust model predictive control (RMPC). Closed-loop RMPC uses uncertainty knowledge to enhance the nominal MPC. The RMPC is shown capable of maintaining the temperature within the comfort zone for model uncertainty up to 70%.

Exergy is relevant to quality of energy and is also a measure of sustainability. Less exergy destruction leads to less of a footprint on the built environment. In this PhD proposal, the exergy concept will be used in the model predictive control cost function (XMPC). The critically new aspects of MPC problem formulation based on exergy are using low quality energy for HVAC systems. In this proposal, exergy destruction is formulated as a cost function of the physical parameters of the model, and the objective is minimization of the calculated exergy destruction rate. Exergy destruction addresses energy consumption, irreversibility and heat losses due to heat transfer. The results show that using the exegy-based objective function is promising, resulting in less electrical energy consumption and exergy destruction.

Many studies have been done to reduce energy consumption and price in buildings and to decrease distribution losses and increase load factor. In this PhD proposal, a new methodology for bidirectional Building to grid (B2G) optimization will be proposed. This bidirectional optimization will lead to bilateral benefits for both buildings and the distribution system.