Prediction of wind speed for the estimation of wind turbine power output from site climatological data using artificial neural network

References

• Akdag, S. A., and O. Guler. 2010. “Wind Characteristics Analysis and Determination of Appropriate Wind Turbine for Amasra-black Sea Region, Turkey.” International Journal of Green Energy 7 (4): 422–433. doi: 10.1080/15435075.2010.493819
   Web of Science ®Google Scholar
• Ayodele, T. R., A. A. Jimoh, J. L. Munda, and J. T. Agee. 2012. “Wind Distribution and Capacity Factor Estimation for Wind Turbines in the Coastal Region of South Africa.” Energy Conversion and Management 64: 614–625. doi: 10.1016/j.enconman.2012.06.007
   Web of Science ®Google Scholar
• Ayodele, T. R., A. A. Jimoh, J. L. Munda, and J. T. Agee. 2013a. “A Statistical Analysis of Wind Distribution and Wind Power Potentials in the Coastal Region of South Africa.” International Journal of Green Energy 10 (6): 1–21.
   Google Scholar
• Ayodele, T. R., A. A. Jimoh, J. L. Munda, and J. T. Agee. 2013b. “Viability and Economic Analysis of Wind Energy Resource for Power Generation in Johannesburg, South Africa.” International journal of Sustainable Energy 33 (2): 284–303. doi: 10.1080/14786451.2012.762777
   Google Scholar
• Bhaskar, M., A. Jain, and N. V. Srinath. 2010. “Wind Speed Forecasting: Present Status.” International Conference on Power System Technology (POWERCON). Hangzhou, China, 1–6.
   Google Scholar
• Chaouachi, A., and K. Nagasaka. 2012. “A Novel Ensemble Neural Network Based Short-term Wind Power Generation Forecasting in a Microgrid.” ISESCO Journal of Science and Technology 8 (14): 2–8.
   Google Scholar
• Chuang, A. S., and C. Schwaegerl. 2009. “ Ancillary Services for Renewable Integration.” Integration of Wide-Scale Renewable Resources into the Power Delivery System, 2009 CIGRE/IEEE PES Joint Symposium, Calgary, AB, 1.
   Google Scholar
• El Chaar, L., L. A. Lamont, and M. Karkoub. 2009. “Modeling Climate Parameters for Renewable Energy Applications in the UAE Using Neural Networks.” IEEE Power and Energy Society CIGRE. Calgary, Canada.
   Google Scholar
• ENERCON. 2011. “ENERCON Wind Turbine Manufacturers.” Accessed July 19. http://www.enercon.de/en-en/Windenergieanlagen.htmhttp://www.enercon.de/en-en/Windenergieanlagen.htm.
   Google Scholar
• Forte, P. M., and J. C. Quadrado. 2005. “Ann Approach to WECS Power Forecast.” International Conference on Emerging Technologies and Factory Automation. Lisboa, Portugal, 1069–1072.
   Google Scholar
• GAMESA. 2011. “GAMESA Cements its Leadership in India.” Accessed July 19. http://www.gamesacorp.com/en/.
   Google Scholar
• GE. 2011. “Wind Turbines-GE Renewable Energy.” Accessed July 20. https://renewables.gepower.com/wind-energy/turbines.htmlhttps://renewables.gepower.com/wind-energy/turbines.html.
   Google Scholar
• Genc, M. S. 2010. “Economic Analysis of Large-Scale Wind Energy Conversion Systems in Central Anatolian Turkey.” In Clean Energy Systems and Experiences, edited by Kei Eguchi, 131–154. Rijeka, Croatia: InTech.
   Google Scholar
• Georgilakis, P. S. 2008. “Technical Challenges Associated with the Integration of Wind Power into Power Systems.” Renewable and Sustainable Energy Reviews 12: 852–863. doi: 10.1016/j.rser.2006.10.007
   Web of Science ®Google Scholar
• Gokcek, M., and M. S. Genc. 2009. “Evaluation of Electricity Generation and Energy Cost of Wind Energy Conversion System (WECS) in Central Turkey.” Applied Energy 86: 2731–2739. doi: 10.1016/j.apenergy.2009.03.025
   Web of Science ®Google Scholar
• Institute for Clinical Evaluative Sciences (ICES). 1996. Advantages and Disadvantages of Using Artificial Neural Networks Versus Logistic Regression for Predicting Medical Outcomes.” Journal of Clinical Epidemiology 49 (11): 1225–1231. doi: 10.1016/S0895-4356(96)00002-9
   PubMed Web of Science ®Google Scholar
• Kelouwani, S., and K. Agbossou. 2004. “Nonlinear Model Identification of Wind Turbine with a Neural Network.” IEEE Transaction on Energy Conversion 19 (3): 607–612. doi: 10.1109/TEC.2004.827715
   Web of Science ®Google Scholar
• Kennedy, B. W. 2004. “Integrating Wind Power: Transmission and Operational Impacts.” Refocus 5 (1): 36–37. doi:10.1016/S1471-0846(04)00075-7.
   Google Scholar
• Lange, M., and U. Focken. 2008. “New Developments in Wind Energy Forecasting.” IEEE Power and Energy Society General Meeting on Conversion and Delivery of Electrical Energy in the 21st Century. Pittsburgh, PA, USA.
   Google Scholar
• Lei, Ma, Luan Shiyan, Jiang Chuanwen, Liu Hongling, and Zhang Yan. 2009. “A Review on the Forecasting of Wind Speed and Generated Power.” Renewable and Sustainable Energy Reviews 13: 915–920. doi: 10.1016/j.rser.2008.02.002
   Web of Science ®Google Scholar
• Li, S., D. C. Wunsch, E. A. O'hair, and M. G. Giesselmann. 2001. “Using Neural Networks to Estimate Wind Turbine Power Generation.” IEEE Transaction on Energy Conversion 16 (3): 276–282. doi: 10.1109/60.937208
   Web of Science ®Google Scholar
• Mao, J., X. Zhang, and J. Li. 2013. “Wind Power Forecasting Based on the BP Neural Network.” 2nd International Conference on System Engineering and Modelling (ICSEM-13), Atlanti Press, France, 13–17.
   Google Scholar
• Nouni, M. R., S. C. Mullick, and T. C. Kandpal. 2007. “Techneconomics of Smallwind Electric Generator Projects for Decentralized Power Supply in India.” Energy Policy 35: 2491–2506. doi: 10.1016/j.enpol.2006.08.011
   Web of Science ®Google Scholar
• Olaofe, Z. O., and K. A. Folly. 2012. “Comparison of the Wind Power Output of a Small Scale Turbine Using Statistical and Feed-forward Neural Networktechniques.” Proceedings of the IASTED International Conference on Power and Energy Systems (AfricaPES 2012), Gaborone, Botswana, 95–101.
   Google Scholar
• Pacudan, R. 2006. “The Challenge of Renewable Energies Integration in Energy Distribution Systems.” In Networked Knowledge for Renewable Energies. Research, Development and Education - Basis for Wide-spread Deployment of Renewable Energies edited by S Wienges, G Stadermann and P Szczepanski, 51–54. Berlin: ForschungsVerbund Sonnenenergie.
   Google Scholar
• Pérez, E. C., A. N. Rodríguez, R. E. M. De La Torre, O. L. Danguillecourt, and J. R. D. Portela. 2012. “Forecast of Wind Speed with a Backpropagation Artificial Neural Network in the Isthmus of Tehuantepec Region in the State of Oaxaca, Mexico.” Acta Universitaria 22: 7–14.
   Google Scholar
• Smith, J. C., E. A. Demeo, B. Parsons, and M. Milligan. 2004. “ Wind Power Impacts on Electric Power System Operating Costs: Summary and Perspective on Work to Date.” American Wind Energy Association Global WindPower Conference, Chicago, Illinois.
   Google Scholar
• VESTAS. 2011. “VESTAS Wind Turbines.” Accessed July 19. http://www.vestas.com/.
   Google Scholar
• WASA. 2011. “ Data Files.” www.wasa.csir.co.za.
   Google Scholar
• Weisser, D., and R. S. Garcia. 2005. “Instantaneous Wind Penetration in Isolated Electricity Grids: Concepts and Review.” Renewable Energy 30: 1299–1308. doi: 10.1016/j.renene.2004.10.002
   Web of Science ®Google Scholar
• Wen-Yeau, C. 2013. “Short-term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method.” Energies 6: 4879–4896. doi: 10.3390/en6094879
   Web of Science ®Google Scholar
• Yeh, T.-H., and L. Wang. 2008. “A Study on Generator Capacity for Wind Turbines Under Various Tower Heights and Rated Wind Speeds Using Weibull Distribution.” IEEE Transactions on Energy Conversion 23 (2): 592–602. doi: 10.1109/TEC.2008.918626
   Web of Science ®Google Scholar
• Zhao, X., S. X. Wang, and T. Li. 2011. “Review of Evaluation Criteria and Main Methods of Wind Power Forecasting.” Energy Procedia 12: 761–769. doi: 10.1016/j.egypro.2011.10.102
   Google Scholar