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Original Articles

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

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References

  • Babu, B.V., and R. Angira. 2006. Modified differential evolution (MDE) for optimization of non-linear chemical processes. Computers and Chemical Engineering 30:989–1002. doi:10.1016/j.compchemeng.2005.12.020.
  • Bagiorgas, H.S., G. Mihalakakou, S. Rehman, and L.M. Al-Hadhrami. 2011. Weibull parameters estimation using four different methods and most energy carrying wind speed analysis. International Journal of Green Energy 8:529–54. doi:10.1080/15435075.2011.588767.
  • Bagiorgas, H.S., G. Mihalakakou, S. Rehman, and L.M. Al-Hadhrami. 2012. Offshore wind speed and wind power characteristics for ten locations in Aegean and Ionian Seas. Journal of Earth System Science 121:975–87. doi:10.1007/s12040-012-0203-9.
  • Bojacá, C.R., H.A. Casilimas, R. Gil, and E. Schrevens. 2012. Extending the input-output energy balance methodology in agriculture through cluster analysis. Energy 47:465–70. doi:10.1016/j.energy.2012.09.051.
  • Cabello, M., and J.A.G. Orza. 2010. Wind speed analysis in the province of Alicante, Spain. Potential for small-scale wind turbines. Renewable and Sustainable Energy Reviews 14:3185–91. doi:10.1016/j.rser.2010.07.002.
  • Chang, T.P. 2011. Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application. Applied Energy 88:272–82. doi:10.1016/j.apenergy.2010.06.018.
  • Dahmouni, A.W., M.B. Salah, F. Askri, C. Kerkeni, and S.B. Nasrallah. 2011. Assessment of wind energy potential and optimal electricity generation in Borj-Cedria, Tunisia. Renewable and Sustainable Energy Reviews 15:815–20. doi:10.1016/j.rser.2010.07.020.
  • Elvira, L.N. 2002. Annual electrical peak load forecasting methods with measures of prediction error. Dissertation Abstracts International 62-10, Section: B: 4719.
  • Garcia, A., J.L. Torres, E. Prieto, and A. De Francisco. 1998. Fitting wind speed distributions: A case study. Solar Energy 62:139–44. doi:10.1016/S0038-092X(97)00116-3.
  • Gokhale, S., and M. Khare. 2007. Statistical behavior of carbon monoxide from vehicular exhausts in urban environments. Environmental Modelling and Software 22:526–35. doi:10.1016/j.envsoft.2006.02.008.
  • Gualtieri, G., and S. Secci. 2011. Wind shear coefficients, roughness length and energy yield over coastal locations in Southern Italy. Renewable Energy 36: 1081–94. doi:10.1016/j.renene.2010.09.001.
  • Guo, Z.H., J. Wu, H.Y. Lu, and J.Z. Wang. 2011. A case study on a hybrid wind speed forecasting method using BP neural network. Knowledge-Based Systems 24:1048–56. doi:10.1016/j.knosys.2011.04.019.
  • Himri, Y., S. Rehman, B. Draoui, and S. Himri. 2008. Wind power potential assessment for three locations in Algeria. Renewable and Sustainable Energy Reviews 12:2495–504. doi:10.1016/j.rser.2007.06.007.
  • Hirose, H. 2007. The mixed trunsored model with applications to SARS. Mathematics and Computers in Simulation 74:443–53. doi:10.1016/j.matcom.2006.06.031.
  • Islam, M.R., R. Saidur, and N.A. Rahim. 2011. Assessment of wind energy potentiality at Kudat and Labuan Malaysia using Weibull distribution function. Energy 36:985–92. doi:10.1016/j.energy.2010.12.011.
  • Kantar, Y.M., and B. Şenoğlu. 2008. A comparative study for the location and scale parameters of the Weibull distribution with given shape parameter. Computers & Geosciences 34:1900–09. doi:10.1016/j.cageo.2008.04.004.
  • Kantar, Y.M., and I. Usta. 2008. Analysis of wind speed distributions: Wind distribution function derived from minimum cross entropy principles as better alternative to Weibull function. Energy Conversion and Management 49:962–73. doi:10.1016/j.enconman.2007.10.008.
  • Keyhani, A., M. Ghasemi-Varnamkhasti, M. Khanali, and R. Abbaszadeh. 2010. An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran. Energy 35:188–201. doi:10.1016/j.energy.2009.09.009.
  • Kiss, P., and I.M. Jánosi. 2008. Comprehensive empirical analysis of ERA-40 surface wind speed distribution over Europe. Energy Conversion and Management 49:2142–51. doi:10.1016/j.enconman.2008.02.003.
  • Lim, H.C., and T.Y. Jeong. 2010. Wind energy estimation of the Wol-Ryong coastal region. Energy 35:4700–09. doi:10.1016/j.energy.2010.09.029.
  • Liu, F.J., and T.P. Chang. 2011. Validity analysis of maximum entropy distribution based on different moment constraints for wind energy assessment. Energy 36:1820–26. doi:10.1016/j.energy.2010.11.033.
  • Liu, H.P., J. Shi, and E. Erdem. 2010. Prediction of wind speed time series using modified Taylor Kriging method. Energy 35:4870–79. doi:10.1016/j.energy.2010.09.001.
  • Morgan, E.C., M. Lackner, R.M. Vogel, and L.G. Baise. 2011. Probability distributions for offshore wind speeds. Energy Conversion and Management 52:15–26. doi:10.1016/j.enconman.2010.06.015.
  • Mu, Y.Q., X.L. Wang, Z.H. Bie, L. Xu, and W.P. Zhu. 2009. Analysis of wind speed probability distribution and wind turbine generator capacity factor. Power System Protection and Control 37:65–70.
  • Rehman, S., and N.M. Al-Abbadi. 2008. Wind shear coefficient, turbulence intensity and wind power potential assessment for Dhulom, Saudi Arabia. Renewable Energy 33: 2653–60. doi:10.1016/j.renene.2008.02.012.
  • Rehman, S., A.M. Mahbub, J.P. Meyer, and L.M. Al-Hadhrami. 2012. Wind speed characteristics and resource assessment using Weibull parameters. International Journal of Green Energy 9:800–14. doi:10.1080/15435075.2011.641700.
  • Safari, B. 2011. Modeling wind speed and wind power distributions in Rwanda. Renewable and Sustainable Energy Reviews 15:925–35. doi:10.1016/j.rser.2010.11.001.
  • Scerri, E., and R. Farrugia. 1996. Wind data evaluation in the Maltese Islands. Renewable Energy 7: 109–14. doi:10.1016/0960-1481(95)00097-6.
  • Soukissian, T. 2013. Use of multi-parameter distributions for offshore wind speed modeling: The Johnson SB distribution. Applied Energy 111:982–1000. doi:10.1016/j.apenergy.2013.06.050.
  • Tolikas, K., and G.D. Gettinby. 2009. Modelling the distribution of the extreme share returns in Singapore. Journal of Empirical Finance 16:254–63. doi:10.1016/j.jempfin.2008.06.006.
  • Xinhua Net. 2012. The installed wind energy of Xinjiang is 8 times in 2012 of that in 2006. http://news.xinhuanet.com/fortune/2012-10/21/c_113440052.htm (accessed October 21, 2012).
  • Yan, L.P., and J.C. Zeng. 2006. Particle swarm optimization with self-adaptive stochastic inertia weight. Computer Engineering and Design 27: 4677–79(in Chinese).
  • Yüzgeç, U. 2010. Performance comparison of differential evolution techniques on optimization of feeding profile for an industrial scale baker’s yeast fermentation process. ISA Transactions 49:167–76. doi:10.1016/j.isatra.2009.10.006.
  • Zhang, J.R., J. Zhang, T.M. Lok, and M.R. Lyu. 2007. A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training. Applied Mathematics and Computation 185:1026–37. doi:10.1016/j.amc.2006.07.025.
  • Zhou, J.Y., E. Erdem, G. Li, and J. Shi. 2010. Comprehensive evaluation of wind speed distribution models: A case study for North Dakota sites. Energy Conversion and Management 51:1449–58. doi:10.1016/j.enconman.2010.01.020.

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