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A flexible neural network-fuzzy data envelopment analysis approach for location optimization of solar plants with uncertainty and complexity

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  • Azadeh, A.
  • Sheikhalishahi, M.
  • Asadzadeh, S.M.

Abstract

This study presents a flexible neuro-fuzzy approach for location optimization of solar plants with possible complexity and uncertainty. The flexible approach is composed of artificial neural network (ANN) and fuzzy data envelopment analysis (FDEA). The intelligent approach of this study is applied for location optimization of solar plants in Iran. First, FDEA is validated by DEA, and then it is used for ranking of solar plant units (SPUs) and the best α-cut is selected based on the test of Normality. Also, several ANNs are developed through multi layer perceptron (MLP) for ranking of solar plants and the best one with minimum mean absolute percentage of error (MAPE) is selected for further considerations. Finally, the preferred model (FDEA or ANN) is selected based on test of Normality. The implementation of the flexible approach for solar plants in Iran identifies the preferred FDEA at α = 0.3, where is the level of data uncertainty. This indicates that the data are collected from the uncertain and fuzzy environment. This is the first study that presents a flexible approach for identification of optimum location of solar plants with possible noise, non-linearity, complexity and environmental uncertainty. This would help policy makers to identify the preferred Strategy for location optimization problems associated with solar plant units.

Suggested Citation

  • Azadeh, A. & Sheikhalishahi, M. & Asadzadeh, S.M., 2011. "A flexible neural network-fuzzy data envelopment analysis approach for location optimization of solar plants with uncertainty and complexity," Renewable Energy, Elsevier, vol. 36(12), pages 3394-3401.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:12:p:3394-3401
    DOI: 10.1016/j.renene.2011.05.018
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    1. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    2. Sözen, Adnan & Arcaklıoğlu, Erol & Özalp, Mehmet & Çağlar, Naci, 2005. "Forecasting based on neural network approach of solar potential in Turkey," Renewable Energy, Elsevier, vol. 30(7), pages 1075-1090.
    3. Ramanathan, R, 2001. "Comparative Risk Assessment of energy supply technologies: a Data Envelopment Analysis approach," Energy, Elsevier, vol. 26(2), pages 197-203.
    4. Sözen, Adnan & Arcaklioglu, Erol, 2005. "Solar potential in Turkey," Applied Energy, Elsevier, vol. 80(1), pages 35-45, January.
    5. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    6. Bahel, V. & Srinivasan, R. & Bakhsh, H., 1986. "Solar radiation for Dhahran, Saudi Arabia," Energy, Elsevier, vol. 11(10), pages 985-989.
    7. Azadeh, A. & Ghaderi, S.F. & Maghsoudi, A., 2008. "Location optimization of solar plants by an integrated hierarchical DEA PCA approach," Energy Policy, Elsevier, vol. 36(10), pages 3993-4004, October.
    8. Özgür Özpeynirci & Murat Köksalan, 2007. "Performance evaluation using data envelopment analysis in the presence of time lags," Journal of Productivity Analysis, Springer, vol. 27(3), pages 221-229, June.
    9. Leduc, S. & Lundgren, J. & Franklin, O. & Dotzauer, E., 2010. "Location of a biomass based methanol production plant: A dynamic problem in northern Sweden," Applied Energy, Elsevier, vol. 87(1), pages 68-75, January.
    10. Sözen, Adnan & Arcaklioglu, Erol & Özalp, Mehmet & Kanit, E. Galip, 2005. "Solar-energy potential in Turkey," Applied Energy, Elsevier, vol. 80(4), pages 367-381, April.
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