Term of Award

Fall 2017

Degree Name

Master of Science in Applied Engineering (M.S.A.E.)

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Department of Mechanical Engineering

Committee Chair

David Calamas

Committee Member 1

Biswanath Samanta

Committee Member 2

Mosfequr Rahman

Abstract

As the effects of burning fossil fuels continues to present its prevalence, the interests in alternative forms of energy is expanding. Within the home, the domestic electrical water heater accounts for approximately 17% of its energy consumption. Reducing the amount of energy required to produce hot water from this thermal system alone can have a significant effect on reducing its carbon footprint. In this presented work, a modeled domestic electrical water heater was supplied photovoltaic and on-grid electrical power to increase its energy efficiency. As photovoltaic (PV) energy is directly related to solar irradiation, it is important to receive accurate solar irradiance data for the area and to forecast future solar irradiance outputs to determine optimal energy input. A Kipp & Zonen solar tracker, capable of Baseline Surface Radiation Network (BSRN) level data collection, was installed on Georgia Southern University’s campus to determine extremely accurate solar irradiance. Future irradiance data based on the historical data was then predicted by using artificial neural network (ANN) methods and those results were used to determine future PV output. The system was evaluated strictly by modeling the PV system and domestic electric water heater (DEWH), and then the PV system was integrated into the operation of the DEWH. In comparison to the typical operation of the DEWH, a PV inclusive DEWH produced a significant decrease in the on-grid energy dependency of the entire system.

Research Data and Supplementary Material

No

Download

Share

COinS