Life cycle assessment of renewable energy in Egypt by 2035 using dynamic bayesian networks /
تقييم دورة حياة الطاقة المتجددة فى مصر بحلول عام 2035 باستخدام شبكة بايزى الديناميكيه
Rawaa Hesham Abdelhay Elbidweihy ; Supervised Ihab A. Elkhodary , Hisham M. Abdelsalam
- Cairo : Rawaa Hesham Abdelhay Elbidweihy , 2021
- 154 P . : charts ; 30cm
Thesis (M.Sc.) - Cairo University - Faculty of Computers and Artificial Intelligence - Department of Operations Research and Decision Support
In an era where machines run and shape our world, the need for a stable, non-ending source of energy emerges. In this study, the focus is on the solar energy in Egypt as a renewable source, the most important factors that could affect the solar energys market share throughout its life cycle production are analyzed and filtered, the relationships between them are derived before structuring a Bayesian network. Also, forecasted models are built for multiple factors to predict their future values in Egypt by 2035, based on historical data and patterns; to be used as the nodes states in the network. 37 factors are found to have a possible impact on the use of solar energy. They are reduced to 12 factors that are chosen to be the most effective to the solar energys life cycle in Egypt, based on surveying experts and data analysis; some of the factors are found to be recurring in multiple stages. The states of the factors in the constructed Bayesian network are indicated based on the followed data distribution type for each factor, and using either the Z-distribution approach or the Cheby shevs inequality theorem Later on, the individual and the conditional probabilities of the states of each factor in the Bayesian network are derived either from the collected and scrapped historical data or from estimations and past studies. Results show that we can use the constructed model for scenario and decision analysis concerning forecasting the total percentage of the market share of the solar energy in Egypt and using it as a stable renewable source for generating any type of energy needed
Bayesian analysis
Dynamic bayesian networks Renewable energy The solar energy