Date of Award:

8-2025

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Electrical and Computer Engineering

Committee Chair(s)

Greg Droge

Committee

Greg Droge

Committee

Mario Harper

Committee

Burak Sarsilmaz

Abstract

As we use more renewable energy, such as solar power, and add new devices, such as electric vehicle chargers and battery storage, to our buildings, the management of electricity becomes more complex. These local energy sources and devices can form small "microgrids" that need careful coordination to work efficiently with the main power grid. The system figures out the best times to use, store or charge different devices (such as batteries and EVs) to avoid costly, high electricity demand spikes and help stabilize the main power grid, especially when asked by the utility company. A major part of this work involved creating better ways to control building heating and cooling (HVAC) systems, which are big energy users. Two methods were developed: one learns from past energy use data to predict how temperature settings affect energy consumption, and another uses simplified physics models to understand how heat moves in a building. By intelligently scheduling all of these devices together, our aim is to make our energy use cheaper, more reliable, and better integrated with renewable resources.

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