@article { author = {Poorahangaryan, Fershteh and Shahbi, Ali and Nabiee, Esmaeel}, title = {The evaluation of renewable energy power using hybrid model of neural network and data envelopment analysis (neuro - DEA)}, journal = {Journal of Applied Research on Industrial Engineering}, volume = {1}, number = {1}, pages = {19-27}, year = {2014}, publisher = {Research Expansion Alliance (REA) on behalf of Ayandegan Institute of Higher Education}, issn = {2538-5100}, eissn = {2676-6167}, doi = {}, 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 = {Data Envelopment Analysis ( DEA),Artificial-Neural Network (ANN)}, url = {https://www.journal-aprie.com/article_43012.html}, eprint = {https://www.journal-aprie.com/article_43012_d9b37824d2c74b4d8b992514efd87c0e.pdf} }