Date of Graduation

2016

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Industrial and Managements Systems Engineering

Committee Chair

Majid Jaridi

Committee Co-Chair

Kenneth Currie

Committee Member

Feng Yang

Abstract

Saudi Arabia is one nation that has been exploring the potential of renewable energy for many years. Saudi authorities, scientists, and researchers view renewable energy as a preferable long-term energy strategy. Despite this, because Saudi Arabia is one of the leading oil producing nations and relies heavily on it as a form of energy, solar energy has not been given much serious consideration. Solar and wind energy are the best sources of renewable energy in Saudi Arabia; however, because of the large amount of oil in the country, most do not want to explore the option of renewable energy. Hence, it is essential to explore the alternative sources to insure reliable supply for potential future need. This will be investigated through this research, in which three different forecasting methods were generated for 32 cities: the decomposition method, multiple linear regressions (linear trend model), and multiple linear models (seasonal model). These three methods have developed a preferred model that can forecast renewable energy in the future. The main objectives of this research are i) to establish the potential of solar and wind energy generation as a suitable, cost-effective alternative to petroleum products and ii) to establish the potential for maximizing renewable power generation to support the grid supply to Saudi cities. The software developed for this thesis (Visual Basic) is aimed at enabling a user to use advanced data analysis techniques to handle a given research issue. Moreover, the results of this research demonstrated that the total of the output of solar and wind power in 32 locations in Saudi Arabia are 162.032 GW, and 1.298 GW, respectively. Thus, in order to reduce the cost of energy, installing renewable farms is recommended.

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