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Scenario and Decision Analysis for Solar Energy in Egypt by 2035 Using Dynamic Bayesian Network

Authors: Rawaa H. El-Bidweihy, Hisham M. Abdelsalam, Ihab A. El-Khodary


Bayesian networks are now considered to be a promising tool in the field of energy with different applications. In this study, the aim was to indicate the states of a previous constructed Bayesian network related to the solar energy in Egypt and the factors affecting its market share, depending on the followed data distribution type for each factor, and using either the Z-distribution approach or the Chebyshev’s inequality theorem. Later on, the separate and the conditional probabilities of the states of each factor in the Bayesian network were derived, either from the collected and scrapped historical data or from estimations and past studies. Results showed that we could use the constructed model for scenario and decision analysis concerning forecasting the total percentage of the market share of the solar energy in Egypt by 2035 and using it as a stable renewable source for generating any type of energy needed. Also, it proved that whenever the use of the solar energy increases, the total costs decreases. Furthermore, we have identified different scenarios, such as the best, worst, 50/50, and most likely one, in terms of the expected changes in the percentage of the solar energy market share. The best scenario showed an 85% probability that the market share of the solar energy in Egypt will exceed 10% of the total energy market, while the worst scenario showed only a 24% probability that the market share of the solar energy in Egypt will exceed 10% of the total energy market. Furthermore, we applied policy analysis to check the effect of changing the controllable (decision) variable’s states acting as different scenarios, to show how it would affect the target nodes in the model. Additionally, the best environmental and economical scenarios were developed to show how other factors are expected to be, in order to affect the model positively. Additional evidence and derived probabilities were added for the weather dynamic nodes whose states depend on time, during the process of converting the Bayesian network into a dynamic Bayesian network.

Keywords: Bayesian network, Chebyshev, decision variable, dynamic Bayesian network, Z-distribution

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[1] “Global Energy Consumption”
[2] B. Sørensen, “Life-cycle analysis of renewable energy systems,” Renew. Energy, vol. 5, no. 5–8, pp. 1270–1277, Aug. 1994, doi: 10.1016/0960-1481(94)90161-9.
[3] A. K. Akella, R. P. Saini, and M. P. Sharma, “Social, economical and environmental impacts of renewable energy systems,” Renew. Energy, vol. 34, no. 2, pp. 390–396, Feb. 2009, doi: 10.1016/j.renene.2008.05.002.
[4] European Commission DG Environment News Alert Service, “Relationships between energy consumption and economic growth investigated,” Renew. Sustain. Energy Rev., vol. 16: 5718–5, no. 312, p. 2013, 2013, (Online). Available:
[5] S. Shafiei and R. A. Salim, “Non-renewable and renewable energy consumption and CO2 emissions in OECD countries: A comparative analysis,” Energy Policy, vol. 66, pp. 547–556, Mar. 2014, doi: 10.1016/j.enpol.2013.10.064.
[6] N. J. Sheikh, D. F. Kocaoglu, and L. Lutzenhiser, “Social and political impacts of renewable energy: Literature review,” Technol. Forecast. Soc. Change, vol. 108, no. May, pp. 102–110, 2016, doi: 10.1016/j.techfore.2016.04.022.
[7] M. Borunda, O. A. Jaramillo, A. Reyes, and P. H. Ibargüengoytia, “Bayesian networks in renewable energy systems: A bibliographical survey,” Renew. Sustain. Energy Rev., vol. 62, no. June, pp. 32–45, 2016, doi: 10.1016/j.rser.2016.04.030.
[8] D. Cinar and G. Kayakutlu, “Scenario analysis using Bayesian networks: A case study in energy sector,” Knowledge-Based Syst., vol. 23, no. 3, pp. 267–276, 2010, doi: 10.1016/j.knosys.2010.01.009.
[9] G. de la Torre-Gea, G. M. Soto-Zarazúa, R. G. Guevara-González, and E. Rico-García, “Bayesian networks for defining relationships among climate factors,” Int. J. Phys. Sci., vol. 6, no. 18, pp. 4412–4418, 2011, doi: 10.5897/IJPS11.631.
[10] Science for Environment Policy, Relationships between energy consumption and economic growth investigated,
[11] K. Kim, H. Park, and H. Kim, “Real options analysis for renewable energy investment decisions in developing countries,” Renewable and Sustainable Energy Reviews, vol. 75. Elsevier Ltd, pp. 918–926, Aug. 01, 2017, doi: 10.1016/j.rser.2016.11.073.
[12] A. Petrillo, F. De Felice, E. Jannelli, C. Autorino, M. Minutillo, and A. L. Lavadera, “Life cycle assessment (LCA) and life cycle cost (LCC) analysis model for a stand-alone hybrid renewable energy system,” Renew. Energy, vol. 95, pp. 337–355, 2016, doi: 10.1016/j.renene.2016.04.027
[13] Z. Liu, Y. Liu, D. Zhang, B. Cai, and C. Zheng, “Fault diagnosis for a solar assisted heat pump system under incomplete data and expert knowledge,” Energy, vol. 87, pp. 41–48, 2015, doi: 10.1016/
[14] G. Li and J. Shi, “Applications of Bayesian methods in wind energy conversion systems,” Renew. Energy, vol. 43, pp. 1–8, 2012, doi: 10.1016/j.renene.2011.12.006
[15] J. A. Carta, S. Velázquez, and J. M. Matías, “Use of Bayesian networks classifiers for long-term mean wind turbine energy output estimation at a potential wind energy conversion site,” Energy Convers. Manag., vol. 52, no. 2, pp. 1137–1149, 2011, doi: 10.1016/j.enconman.2010.09.008
[16] P. H. Ibargüengoytia et al., “Wind power forecasting using dynamic Bayesian models,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8857, pp. 184–197, 2014, doi: 10.1007/978-3-319-13650-9_17
[17] A. S. Cofino, R. Cano, C. Sordo, and J. M. Gutierrez, “Bayesian Networks for Probabilistic Weather Prediction,” Proc. 15th Eur. Conf. Artif. Intell., vol. 700, pp. 695–700, 2002, (Online). Available:
[18] S. Hellman, A. McGovern, and M. Xue, “Learning ensembles of continuous Bayesian networks: An application to rainfall prediction,” Proc. - 2012 Conf. Intell. Data Understanding, CIDU 2012, pp. 112–117, 2012, doi: 10.1109/CIDU.2012.6382191
[19] G. L. L. L. Dong, W. Zhou; Pei Zhang, “Short-term photovoltaic output forecast based on Dynamic Bayesian Network theory,” Proc. Chinese Soc. Electr. Eng., vol. 33, pp. 38–45, 2013.
[20] D. Gambelli, F. Alberti, F. Solfanelli, D. Vairo, and R. Zanoli, “Third generation algae biofuels in Italy by 2030: A scenario analysis using Bayesian networks,” Energy Policy, vol. 103, no. April 2016, pp. 165–178, 2017, doi: 10.1016/j.enpol.2017.01.013.
[21] R. H. El-bidweihy, H. M. Abdelsalam, and I. A. El-khodary, “Constructing a Bayesian Network for Solar Energy in Egypt Using Life Cycle Analysis and Machine Learning Algorithms,” Int. J. Energy Power Eng., vol. 14, no. 10, pp. 310–319, 2020.
[22] “Empirical Rule – Financial Analysis,” Investopedia.,standard%20deviations%20from%20the%20mean.
[23] J. Mitchel, “Notes on the Chebyshev Inequality,” vol. 318, no. X, p. 318.
[24] “Chebyshev Inequality,” ScienceDirect.
[25] U. Kjærulff and L. C. van der Gaag, “Making Sensitivity Analysis Computationally Efficient,” no. March 1999, 2013, (Online). Available:
[26] “Egypt Gasoline Prices,” Trading Economics.
[27] “Egypt GDP per capita,” Trading Economics.
[28] “Egypt Crude Oil: Production,” CEIC Data.
[29] “Egypt Oil,” WorldOMeter.
[30] “Egypt Energy Situation,” EnergyPedia.
[31] “Egypt Personal Income Tax Rate,” Trading Economics.
[32] “Cairo Historical Weather,” WorldWeatherOnline.
[33] Rawaa El Bidweihy, “GithubRepository.”