The evaluation of renewable energy power using hybrid model of neural network and data envelopment analysis (neuro - DEA)

Authors

Faculty of engineering, Ayandegan Institute of Higher Education, Tonekabon, Iran.

Abstract

Energy is essential parameter for economic – social development and quality of life. Sustainable energy is requisite for any economic growth. Nowadays, new options for producing energyand using technologies for its production are reproducible. So, the choice of technology is very important. In this article, 6 different renewable powers has evaluated using Hybrid model of Artificial-Neural Network (ANN) and data envelopment analysis base on economic- technical indicators. Because, the low number of inputs and outputs of decision making units, (DMUs), leading to a reduction a separable power of DMUs at traditional DEA, so the NEURO-DEA was used the simulation results shows that off-shore wind energy have high efficiency rather than other studied energy.

Keywords


[1] EIA, U. S. Energy Consumption by Energy Source, U.S. Energy Information Administration, 2010. (www.eia.doe.gov)

[2] Bartz, Sherry., Kelly, David L . "Economic growth and the environment:Theory and facts", Science Direct, Resource and Energy Economics, vol 30 , pp 115–14, (2008).

[3] Pollin, Robert ., Heintz, James., Garrett-Peltier, Heidi .The Economic Bene its of Investing in Clean Energy, Department of Economics and Political Economy Research Institute (PERI)University of Massachusetts, Amherst, (2009).

[4] Charnes . A, W. W. Cooper and E. Rhodes, ”Measuring the efficientof decision making unit”, European Journal of Operation Research, 2,429-444, (1978).

[5] Liu,CH., Lin, SJ., Lewis, C.,” Evaluation of thermal power plant operational performance in Taiwan by dataenvelopment analysis”, Energy policy, Vol. 28, No. 2, pp. 1049-1058, (2009).

[6] Sozen, A., Alp, I., Ozdemir, A.,” Evaluation of thermal power plant operational performance in Taiwan by data envelopment analysis”, Energy policy, Vol. 38, No. 10, pp.6194-6203, (2010).

[7] Sueyoshi, T., Goto, M., “Returns to scale vs. damages to scale in data envelopment analysis: An impact of U.S. clean air act on coal-fired power plants”, Omega, In Press, Corrected Proof, Available online 14 February (2012).

[8] A. Azadeh, S.F. Ghaderi and M.R. Nasrollahi, “Location optimization of wind plants in Iran by an integrated hierarchical Data Envelopment Analysis,” Renewable Energy, vol. 36, pp. 1621-1631, (2011).

[9] Khalaf Al-Delaimi, K. S., Al- Ani, A. H. B , " using data envelopment analysis to measure cost efficiency with an application on Islamic Banks". Scientific Journal of Administrative Development, V4, 134- 156.(2006). [10]Afzal, M. N. I., Roger Lawrey, R A Measurement Framework for Knowledge-Based Economy (KBE) Efficiency in ASEAN: A Data Envelopment (DEA) Window Approach. International Journal of Business and Management, 7(18), 57-68, (2012).

[11]l.fausett, "fundamental of neural network: architecture, algorithms, and application", prentice-hall,(1994).

[12]s.haykin,"neural network: a comprehensive foundation, "second edition, prentice-hall,(1999).

[13]seyyed reza shah amiri." Introduction to Neural Networks", Electronic Journal of Iranian Research Institute for Scientific Information and Documentation, Vol. 6, No. 1,(2006).

[14]Athnossopulos A., Curram S. "A comparison of data envelopment analysis and artificial neural networks as tool for assessing the efficiency of decision making units", Journal of the operation research society 47, p1000-1016. (1996).

[15]P. Lako, “Technical and economic features of renewable electricity Technologies,” Tech. Rep. ECN-E--10- 034, (2010).