Determination of Wind Potential of a Specific Region using Artificial Neural Networks
DOI:
https://doi.org/10.18201/ijisae.2017531433Keywords:
Artificial Neural Networks (ANN), Wind Potential, Estimation, Wind PowerAbstract
There is a widespread trend in alternative energy sources in today's world. Achieving energy without harming the environment has been the most important target of the countries in recent years. For this reason, it is necessary to make utmost use of natural energy sources such as wind, sun and water. Among these sources, wind energy is the most utilized. Because it was cheap and quickly return to investment it is carried out many studies in this area. However, the most important problem is the continuity when the wind energy is obtained. The first thing to do before a wind power plant is installed in a region is to calculate the wind potential of the area concerned. This process is long-term under normal conditions. Artificial Neural Networks (ANN) is one of the most frequently used methods for determining a wind power potential in a short time period. In this study, it is aimed to estimate the wind potential of a certain region within the boundaries of Osmaniye province. ANN was used to estimate the wind power potential. As a result of comparing the statistical values of the forecast values with the measured actual values, the performance of the method applied is indicated. The meteorology station at Osmaniye Korkut Ata University using data has been successfully estimated wind potential.Downloads
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Y. Kassa, J. H. Zhang, D. H. Zheng, and D. Wei, “A GA-BP hybrid algorithm based ANN model for wind power prediction,” 2016 IEEE Smart Energy Grid Engineering (SEGE), pp. 158-163, 2016.
GLOBAL WIND ENERGY OUTLOOK 2016 - Global Status Report - GWEC.
A. R. Finamore, V. Calderaro,V. Galdi, A. Piccolo, and G. Conio, “A Wind Speed Forecasting Model Based on Artificial Neural Network and Meteorological Data,” 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), pp. 1-5, 2016.
İ. Kirbaş, and A. Kerem, “Short-Term Wind Speed Prediction Based on Artificial Neural Network Models,” Sage Journals, 2016.
B. Wu, Y. Lang, N. Zargari, and S. Kouro, Power Conversion and Control of Wind Energy Systems, IEEE, New Jersey: John Wiley&Sons, 2011.
Z. W. Zhenga, Y. Y. Chena, X. W. Zhoua, M. M. Huoa, B. Zhaoc, and M. Y. Guod, “Short-Term Wind Power Forecasting Using Empirical Mode Decomposition and RBFNN,” International Journal of Smart Grid and Clean Energy, vol. 2, pp. 192–199, May. 2013.
G. Li, and J. Shi, “On comparing three artificial neural networks for wind speed forecasting,” Applied Energy, vol. 87, pp. 2313-2320, July. 2010.
M. C. Mabel, and E. Fernandez, “Analysis of wind power generation and prediction using ANN: A case study,” Renewable Energy, vol. 33, pp. 986-99, May. 2008.
M. Lei, L. Shiyan, and J. Chuanwen, “A review on the forecasting of wind speed and generated power,” Renewable and Sustainable Energy Reviews, vol. 13, pp. 915-920, May. 2009.
S. S. Soman, H. Zareipour, O. Malik, and P. Mandal, “A Review of Wind Power and Wind Speed Forecasting Methods With Different Time Horizons,” IEEE Xplore Conference: North American Power Symposium (NAPS), 2010.
G. Giebel, R. Brownsword, G. Kariniotakis, M. Denhard, and C. Draxl, “The state-of-the-art in short-term prediction of wind power: A literature overview”, ANEMOS. Plus, 2011.
Ç. Elmas, Yapay Zeka Uygulamaları, 2 nd ed., Ed. Ankara, Türkiye: Seçkin Yayıncılık, 2010.
Ş. Taşdemir and A. C. Çınar, "Application of Artificial Neural Network Forecasting of Daily Maximum Temperature in Konya," MENDEL'2011 17th International Conference on Soft Computing Conference, pp. 236-243, June. 2011.
B. Yanktepe, and Y.A. Genc, “Establishing new model for predicting the global solar radiation on horizontal surface,” International Journal of Hydrogen Energy, vol. 40, pp. 15278-1528326, Nov. 2015.
B. Yanıktepe, S. Taşdemir, A.B. Güher, and S. Akcan, "Wind Power Forecasting For The Province Of Osmaniye Using Artificial Neural Network Method," International Journal of Intelligent Systems and Applications in Engineering, vol. 4-Special İssue-1, pp. 114-117, 2016.
R. Velo, P. López, and F. Maseda, “Wind speed estimation using multilayer perceptron,” Energy Conversion and Management, vol. 81, pp. 1-9, May. 2014.
M. Bilgili, B. Sahin, and A. Yasar, “Application of artificial neural networks for the wind speed prediction of target station using reference stations data,” Renewable Energy, vol. 32, pp. 2350-2360, Nov. 2007.
S. Tasdemir, I. Saritas, M. Ciniviz, and N. Allahverdi, Artificial Neural Network and Fuzzy Expert System Comparison for Prediction of Performance and Emission Parameters on a Gasoline Engine, Expert Systems with Applications (ISI), vol. 29, pp. 1471-1480, 2012.
A.E. Tumer, and S. Edebali, "An Artificial Neural Network Model for Wastewater Treatment Plant of Konya," International Journal of Intelligent Systems and Applications in Engineering, vol. 3(4), pp. 131-135, 2015.
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