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Date of Award


Document Type

Master's Thesis

Degree Name

Master of Science in Mechanical Engineering (MS)

College, School or Department Name

Department of Mechanical Engineering-Engineering Mechanics

First Advisor

Mahdi Shahbakhti


Nowadays, buildings with smart grid interaction are a new platform that allows implementation of innovative control technology in order to save energy and reduce cost of energy. It connects technology to the building environment making it beneficial to the residents of the building as well as the environment outside the building. The feature dynamic pricing of the smart grid leads to smart use of electricity in a building allowing shutdown and start-up of appliances based on high and low peak periods of dynamic pricing, respectively. Due to large HVAC energy consumption particularly heating cost during winters in the office buildings at Michigan Technological University, the thesis focuses on optimizing the energy use for HVAC system. A mathematical energy model pertaining to HVAC system of the building is developed in this thesis. Model Predictive Control (MPC) is implemented on the building energy model to develop two controllers having different cost functions, namely minimize power consumption and minimize price of power consumption. The data used for the building energy model is collected from one of the office buildings in Michigan Technological University. Both MPC controllers are compared to the existing On/Off controller in the building to determine the better controller. Further, the model is extended to six buildings connected to the same node in a smart grid. Algorithm of the better MPC controller is modified in order to ensure that the total power consumption (HVAC and Non- HVAC) of six buildings lies within the maximum allowable load at the node. Results demonstrate that MPC benefits the consumer as well as keeps the peak loads on the grid under limit.