Energy Planning Decision-Making Under Uncertainty Based on the Evidential Reasoning Approach

  • Hamza SellakEmail author
  • Brahim Ouhbi
  • Bouchra Frikh
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 616)


In the last two decades, energy planning decision-making (EPDM), especially the evaluation and prioritization of renewable energy sources (RES), has attracted significant attention. The decision-making process is aligned with several sources that can be uncertain, including incomplete information, limited domain knowledge from decision-makers, and failures to provide accurate judgments from experts. In this study, the Evidential Reasoning (ER) approach is developed to manage the expanding complexities and uncertainties in assessment problems. The ER approach is employed as a multiple criteria framework to assess the appropriateness regarding the use of different renewable energy technologies. A case study is provided to illustrate the implementation process. Results show that using the ER approach when assessing the sustainability of different RES under uncertainty allows providing robust decisions, which brings out a more accurate, effective, and better-informed EPDM tool to conduct the evaluation process.


Renewable energy sources Assessment problems Uncertainty Evidential reasoning approach Multiple criteria decision-making 


  1. 1.
    Başar, Ö., Uğurlu, S., Kahraman, C.: Assessment of green energy alternatives using fuzzy ANP. In: Cavallaro, F. (ed.) Assessment and Simulation Tools for Sustainable Energy Systems. Green Energy and Technology Series, vol. 129, pp. 55–77. Springer, London (2013)CrossRefGoogle Scholar
  2. 2.
    Oberti, P., Muselli, M., Haurant, P.: Photovoltaic plants selection on an insular grid using multi-criteria outranking tools: application in Corsica Island (France). In: Cavallaro, F. (ed.) Assessment and Simulation Tools for Sustainable Energy Systems. Green Energy and Technology Series, vol. 129, pp. 27–54. Springer, London (2013)CrossRefGoogle Scholar
  3. 3.
    Banos, R., Manzano-Agugliaro, F., Montoya, F.G., Gil, C., Alcayde, A., Gómez, J.: Optimization methods applied to renewable and sustainable energy: a review. Renew. Sustain. Energy Rev. 15(4), 1753–1766 (2011). ElsevierCrossRefGoogle Scholar
  4. 4.
    Pohekar, S.D., Ramachandran, M.: Application of multi-criteria decision making to sustainable energy planning—a review. Renew. Sustain. Energy Rev. 8(4), 365–381 (2004). ElsevierCrossRefGoogle Scholar
  5. 5.
    Wang, J.J., Jing, Y.Y., Zhang, C.F., Zhao, J.H.: Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renew. Sustain. Energy Rev. 13(9), 2263–2278 (2009). ElsevierCrossRefGoogle Scholar
  6. 6.
    Taha, R.A., Daim, T.: Multi-criteria applications in renewable energy analysis, a literature review. In: Daim, T., Oliver, T., Kim, J. (eds.) Research and Technology Management in the Electricity Industry, pp. 17–30. Springer, London (2013)CrossRefGoogle Scholar
  7. 7.
    Strantzali, E., Aravossis, K.: Decision making in renewable energy investments: a review. Renew. Sustain. Energy Rev. 55, 885–898 (2016). ElsevierCrossRefGoogle Scholar
  8. 8.
    Troldborg, M., Heslop, S., Hough, R.L.: Assessing the sustainability of renewable energy technologies using multi-criteria analysis: suitability of approach for national-scale assessments and associated uncertainties. Renew. Sustain. Energy Rev. 39, 1173–1184 (2014). ElsevierCrossRefGoogle Scholar
  9. 9.
    Kabak, Ö., Cinar, D., Hoge, G.Y.: A cumulative belief degree approach for prioritization of energy sources: case of Turkey. In: Cavallaro, F. (ed.) Assessment and Simulation Tools for Sustainable Energy Systems, pp. 129–151. Springer, London (2013)CrossRefGoogle Scholar
  10. 10.
    Suganthi, L., Iniyan, S., Samuel, A.A.: Applications of fuzzy logic in renewable energy systems–a review. Renew. Sustain. Energy Rev. 48, 585–607 (2015). ElsevierCrossRefGoogle Scholar
  11. 11.
    Kaya, T., Kahraman, C.: Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: the case of Istanbul. Energy 35(6), 2517–2527 (2010). ElsevierMathSciNetCrossRefGoogle Scholar
  12. 12.
    Tasri, A., Susilawati, A.: Selection among renewable energy alternatives based on a fuzzy analytic hierarchy process in Indonesia. Sustain. Energy Technol. Assessments 7, 34–44 (2014). ElsevierCrossRefGoogle Scholar
  13. 13.
    Shafiee, M.: A fuzzy analytic network process model to mitigate the risks associated with offshore wind farms. Expert Syst. Appl. 42(4), 2143–2152 (2015). ElsevierCrossRefGoogle Scholar
  14. 14.
    Jiang, J., Li, X., Zhou, Z.J., Xu, D.L., Chen, Y.W.: Weapon system capability assessment under uncertainty based on the evidential reasoning approach. Expert Syst. Appl. 38(11), 13773–13784 (2011). ElsevierGoogle Scholar
  15. 15.
    Yang, J.B., Sen, P.: A general multi-level evaluation process for hybrid MADM with uncertainty. IEEE Trans. Syst. Man Cybern. 24, 1458–1473 (1994). IEEECrossRefGoogle Scholar
  16. 16.
    Yang, J.B., Singh, M.G.: An evidential reasoning approach for multiple attribute decision making with uncertainty. IEEE Trans. Syst. Man Cybern. 24, 1–18 (1994). IEEECrossRefGoogle Scholar
  17. 17.
    Yang, J.B., Wang, Y.M., Xu, D.L., Chin, K.S.: The evidential reasoning approach for MCDA under both probabilistic and fuzzy uncertainties. Eur. J. Oper. Res. 171, 309–343 (2006). ElsevierMathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Yang, J.B., Xu, D.L.: On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 32(3), 289–304 (2002). IEEECrossRefGoogle Scholar
  19. 19.
    Sellak, H., Ouhbi, B., Frikh, B.: Towards an intelligent decision support system for renewable energy management. In: The 15th International Conference on Intelligent systems Design and Applications. IEEE, Marrakesh (2015)Google Scholar
  20. 20.
    Xu, D.L.: Assessment of nuclear waste repository options using the ER approach. Int. J. Inf. Technol. Decis. Making 8(03), 581–607 (2009). World scientificCrossRefGoogle Scholar
  21. 21.
    Shafer, G.: A Mathematical Theory of Evidence, vol. 1. Princeton University Press, Princeton (1976)zbMATHGoogle Scholar
  22. 22.
    Wang, Y.M., Yang, J.B., Xu, D.L.: Environmental impact assessment using the evidential reasoning approach. Eur. J. Oper. Res. 174(3), 1885–1913 (2006). ElsevierCrossRefzbMATHGoogle Scholar
  23. 23.
    Wang, Y.M., Elhag, T.M.: Evidential reasoning approach for bridge condition assessment. Expert Syst. Appl. 34(1), 689–699 (2008). ElsevierCrossRefGoogle Scholar
  24. 24.
    Kong, G., Xu, D.L., Yang, J.B., Ma, X.: Combined medical quality assessment using the evidential reasoning approach. Expert Syst. Appl. 42(13), 5522–5530 (2015). ElsevierCrossRefGoogle Scholar
  25. 25.
    Stein, E.W.: A comprehensive multi-criteria model to rank electric energy production technologies. Renew. Sustain. Energy Rev. 22, 640–654 (2013). ElsevierCrossRefGoogle Scholar
  26. 26.
    Scottish Government: 2020 Renewable Route Map for Scotland—Update. Scottish Government, Edinburgh (2012)Google Scholar
  27. 27.
    Zhang, Z.J., Yang, J.B., Xu, D.L.: A hierarchical analysis model for multiobjective decision making. In: Analysis, Design and Evaluation of Man–Machine System 1989 (Selected Papers from the 4th IFAC/IFIP/IFORS/IEA Conference, Xian, PR China, September 1989), Pergamon, Oxford, UK, 1990, pp. 13–18 (1989)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.LM2I Laboratory, ENSAMMoulay Ismaïl UniversityMeknesMorocco
  2. 2.LTTI Laboratory, ESTFSidi Mohamed Ben Abdellah UniversityFezMorocco

Personalised recommendations