Date of Award:

8-2020

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Committee Chair(s)

Nicholas Flann

Committee

Nicholas Flann

Committee

John Edwards

Committee

Dan Watson

Abstract

To defeat global warming, the world expects to look at renewable energy sources. Solar energy is one of the best renewable energy sources which causes no harm to the environment. As solar energy changes with atmospheric parameters like temperature, relative humidity, cloud coverage, dewpoint, sun position, day of the year, etc. It is difficult to understand its nature by science. Predicting solar irradiance which is directly proportional to solar energy using atmospheric parameters is the main goal of this work. Powerful artificial intelligence algorithms that won many coding competitions have been used to predict it. Using these methods and numerical weather forecast datasets one can predict solar irradiance up to ten days with the resolution of three hours. Two-day prediction is more reliable as error after that increases.

As solar energy is not available all day there is a need to pre-plan the storage and utilization. From an electric charge station perspective, if he knows the energy generated by solar and the amount of load he needs to supply, he can take a wise decision to supply the maximum load with the available power. This will make him get more profits. This experimental study has been executed by driving solar energy predictions along with load predictions to an algorithm that gives an optimum charge and discharge schedule of the battery considering the profit of the electric vehicle charging station. Profit is calculated with solar predictions in different scenarios with the consideration of the price of the energy at a given time.

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