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Predicting Football Match Results by Machine Learning Methods

Behradfar, Mohsen | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56195 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Rafiee, Majid
  7. Abstract:
  8. Nowadays, electrical energy is one of the crucial requirements in human beings’ lives. Since the majority of the world’s energy is met by fossil fuel, the problems such as global warming, the reduction of fossil fuel resources, and unpredictable oscillation of the prices of such fuels have led to a serious crisis for the people of the world. Furthermore, due to the increasing energy demand, the economic development of most countries has a strong correlation with fossil fuel prices. The abovementioned problems have made a lot of countries take alternative policies in terms of generating energy, one of which is using renewable energy resources. This approach is reported to be clean and affordable in comparison with traditional energy generation methods. Because of having appropriate weather conditions, Iran has the capability of utilizing photovoltaic panels and wind turbines to produce energy. The present study is looking for finding the optimal combination of photovoltaic panels, wind turbines, and energy storage devices, existing in an isolated power plant, to feed the load of an industrial area in Iran. The main aims of the present work are to minimize the economical and technical concerns of the unit, which are respectively minimizing the cost, and maximizing the reliability. Given the disagreement of such goals, the multi-objective approach, utilizing non-dominated sorting genetic algorithm-ii has been proposed to simultaneously optimize these objectives. The numerical results illustrate the effectiveness of the proposed method and the usefulness of using renewable energy resources in generating electrical energy
  9. Keywords:
  10. Machine Learning ; Prediction Models ; Football ; Artificial Neural Network ; Linear Discriminant Analysis ; Random Forest Algorithm ; Ranger Random Forest

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