Evaluation of wind resource potential using statistical analysis of probability density functions in New South Wales, Australia

References

• Akpinar, E. K., and S. Akpinar. 2005. A statistical analysis of wind speed data used in installation of wind energy conversion systems. Energy Conversion and Management 46:515–32. doi:10.1016/j.enconman.2004.05.002.
   Web of Science ®Google Scholar
• Al-Abbadi, N. M. 2005. Wind energy resource assessment for five locations in Saudi Arabia. Renewable Energy 30:1489–99.
   Web of Science ®Google Scholar
• Alavi, O., A. Sedaghat, and A. Mostafaeipour. 2016. Sensitivity analysis of different wind speed distribution models with actual and truncated wind data: A case study for Kerman, Iran. Energy Conversion and Management 120:51–61. doi:10.1016/j.enconman.2016.04.078.
   Web of Science ®Google Scholar
• Alavi, O., K. Mohammadi, and A. Mostafaeipour. 2016. Evaluating the suitability of wind speed probability distribution models: A case of study of east and southeast parts of Iran. Energy Conversion and Management 119:101–08. doi:10.1016/j.enconman.2016.04.039.
   Web of Science ®Google Scholar
• Allouhi, A., O. Zamzoum, M. Islam, R. Saidur, T. Kousksou, A. Jamil, and A. Derouich. 2017. Evaluation of wind energy potential in Morocco’s coastal regions. Renewable and Sustainable Energy Reviews 72:311–24. doi:doi:10.1016/j.rser.2017.01.047
   Google Scholar
• Bagavathsingh, A., C. Srinivas, R. Baskaran, B. Venkatraman, and M. Sardar. 2016. Wind direction dependent vertical wind shear and surface roughness parameter in two different coastal environments. Proceedings of the international conference on radiological safety in workplace, nuclear facilities and environment: book of abstracts, Kalpakkam, India.
   Google Scholar
• Bai, J., and S. Ng. 2005. Tests for skewness, kurtosis, and normality for time series data. Journal of Business & Economic Statistics 23:49–60. doi:10.1198/073500104000000271.
   Web of Science ®Google Scholar
• Balanda, K. P., and H. MacGillivray. 1988. Kurtosis: A critical review. The American Statistician 42:111–19.
   Web of Science ®Google Scholar
• Brano, V. L., A. Orioli, G. Ciulla, and S. Culotta. 2011. Quality of wind speed fitting distributions for the urban area of Palermo, Italy. Renewable Energy 36:1026–39. doi:10.1016/j.renene.2010.09.009.
   Web of Science ®Google Scholar
• Brys, G., M. Hubert, and A. Struyf. 2004. A robust measure of skewness. Journal of Computational and Graphical Statistics 13:996–1017. doi:10.1198/106186004X12632.
   Web of Science ®Google Scholar
• Brys, G., M. Hubert, and A. Struyf. 2006. Robust measures of tail weight. Computational Statistics & Data Analysis 50:733–59. doi:10.1016/j.csda.2004.09.012.
   Web of Science ®Google Scholar
• Bury, K. 1999. Statistical distributions in engineering. New York: Cambridge University Press.
   Google Scholar
• Carta, J. A., P. Ramirez, and S. Velazquez. 2009. A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands. Renewable and Sustainable Energy Reviews 13:933–55.
   Web of Science ®Google Scholar
• Celik, A. N. 2003. Energy output estimation for small-scale wind power generators using Weibull-representative wind data. Journal of Wind Engineering and Industrial Aerodynamics 91:693–707. doi:10.1016/S0167-6105(02)00471-3.
   Web of Science ®Google Scholar
• Chai, T., and R. R. Draxler. 2014. Root mean square error (RMSE) or mean absolute error (MAE)? Geoscientific Model Development Discussions 7:1525–34. doi:10.5194/gmdd-7-1525-2014.
   Google Scholar
• Department of the Environment and Energy, Australian Government. 2018. Australian Energy Update, August.
   Google Scholar
• Elamouri, M., and F. B. Amar. 2008. Wind energy potential in Tunisia. Renewable Energy 33:758–68. doi:10.1016/j.renene.2007.04.005.
   Web of Science ®Google Scholar
• Erdem, E., and J. Shi. 2011. ARMA based approaches for forecasting the tuple of wind speed and direction. Applied Energy 88:1405–14. doi:10.1016/j.apenergy.2010.10.031.
   Web of Science ®Google Scholar
• Fazelpour, F., N. Soltani, and M. A. Rosen. 2015. Wind resource assessment and wind power potential for the city of Ardabil. Iran. International Journal of Energy and Environmental Engineering. 6:431–38. doi:10.1007/s40095-014-0139-8.
   Web of Science ®Google Scholar
• Fazelpour, F., N. Soltani, S. Soltani, and M. A. Rosen. 2015. Assessment of wind energy potential and economics in the north-western Iranian cities of Tabriz and Ardabil. Renewable and Sustainable Energy Reviews 45:87–99. doi:10.1016/j.rser.2015.01.045.
   Web of Science ®Google Scholar
• Forbes, C., M. Evans, N. Hastings, and B. Peacock. 2011. Statistical distributions. Hoboken, New Jersey: John Wiley & Sons.
   Google Scholar
• Gökçek, M., A. Bayülken, and Ş. Bekdemir. 2007. Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey. Renewable Energy 32:1739–52.
   Web of Science ®Google Scholar
• Gualtieri, G., and S. Secci. 2014. Extrapolating wind speed time series vs. Weibull distribution to assess wind resource to the turbine hub height: A case study on coastal location in Southern Italy. Renewable Energy 62:164–76. doi:10.1016/j.renene.2013.07.003.
   Web of Science ®Google Scholar
• Guerri, O., A. Dali, S. M. Boudia, and N. Yassaa. 2020. Performance evaluation of a wind farm using different power density distributions. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 1–13. doi:10.1080/15567036.2020.1775727.
   Web of Science ®Google Scholar
• Hallgren, W., U. B. Gunturu, and A. Schlosser. 2014. The potential wind power resource in Australia: A new perspective. PloS One 9:e99608. doi:10.1371/journal.pone.0099608.
   PubMed Web of Science ®Google Scholar
• Hussain, M. 2002. Dependence of power law index on surface wind speed. Energy Conversion and Management 43:467–72. doi:10.1016/S0196-8904(01)00032-2.
   Web of Science ®Google Scholar
• Irwanto, M., N. Gomesh, M. Mamat, and Y. Yusoff. 2014. Assessment of wind power generation potential in Perlis. Malaysia. Renewable and Sustainable Energy Reviews. 38:296–308. doi:10.1016/j.rser.2014.05.075.
   Web of Science ®Google Scholar
• Janajreh, I., L. Su, and F. Alan. 2013. Wind energy assessment: Masdar City case study. Renewable Energy 52:8–15. doi:10.1016/j.renene.2012.09.025.
   Web of Science ®Google Scholar
• Joanes, D., and C. Gill. 1998. Comparing measures of sample skewness and kurtosis. Journal of the Royal Statistical Society: Series D (The Statistician) 47:183–89. doi:10.1111/1467-9884.00122.
   Google Scholar
• Kamau, J., R. Kinyua, and J. Gathua. 2010. 6 years of wind data for Marsabit, Kenya average over 14 m/s at 100 m hub height; An analysis of the wind energy potential. Renewable Energy 35:1298–302. doi:10.1016/j.renene.2009.10.008.
   Web of Science ®Google Scholar
• Katsigiannis, Y. A., and G. S. Stavrakakis. 2014. Estimation of wind energy production in various sites in Australia for different wind turbine classes: A comparative technical and economic assessment. Renewable Energy 67:230–36. doi:10.1016/j.renene.2013.11.051.
   Web of Science ®Google Scholar
• Khahro, S. F., K. Tabbassum, A. M. Soomro, L. Dong, and X. Liao. 2014. Evaluation of wind power production prospective and Weibull parameter estimation methods for Babaurband. Sindh Pakistan. Energy Conversion and Management. 78:956–67. doi:10.1016/j.enconman.2013.06.062.
   Web of Science ®Google Scholar
• Khlaifat, N., A. Altaee, J. Zhou, Y. Huang, and A. Braytee. 2020. Optimization of a small wind turbine for a rural area: A case study of Deniliquin, New South Wales, Australia. Energies 13:2292.
   Web of Science ®Google Scholar
• Laban, O. N., C. M. Maghanga, and K. Joash. 2019. Determination of the surface roughness parameter and wind shear exponent of Kisii region from the on-site measurement of wind profiles. Journal of Energy 2019. doi:10.1155/2019/8264061
   Google Scholar
• MacGillivray, H., and K. Balanda. 1988. The relationships between skewness and kurtosis. Australian Journal of Statistics 30:319–37. doi:10.1111/j.1467-842X.1988.tb00626.x.
   Google Scholar
• Manwell, J. F., J. G. McGowan, and A. L. Rogers. 2010. Wind energy explained: Theory, design and application. United Kingdom: John Wiley & Sons.
   Google Scholar
• Mardia, K. V. 1970. Measures of multivariate skewness and kurtosis with applications. Biometrika 57:519–30. doi:10.1093/biomet/57.3.519.
   Web of Science ®Google Scholar
• Masseran, N. 2015. Evaluating wind power density models and their statistical properties. Energy 84:533–41. doi:10.1016/j.energy.2015.03.018.
   Web of Science ®Google Scholar
• Masseran, N., A. M. Razali, and K. Ibrahim. 2012. An analysis of wind power density derived from several wind speed density functions: The regional assessment on wind power in Malaysia. Renewable and Sustainable Energy Reviews 16:6476–87. doi:10.1016/j.rser.2012.03.073.
   Web of Science ®Google Scholar
• Maunsell, D., T. Lyons, and J. Whale. 2004. Wind resource assessment of a site in Western Australia.
   Google Scholar
• Mezidi, A., O. Guerri, S. M. Boudia, and K. Mohammedi. 2020. Influence of wind data temporal variation in wind resource assessment. Two case studies in the southern part of Algeria. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 42:161–75. doi:10.1080/15567036.2019.1587063.
   Web of Science ®Google Scholar
• Mills, D. 2001. Assessing the potential contribution of renewable energy to electricity supply in Australia: A study of renewable energy with a particular focus upon domestic rooftop photovoltaics, domestic solar hot water and commercial wind energy.
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• Morgan, V. T. 1995. Statistical distributions of wind parameters at Sydney. Australia. Renewable Energy. 6:39–47. doi:10.1016/0960-1481(94)E0017-Y.
   Web of Science ®Google Scholar
• Okorie, M. E., F. Inambao, and Z. Chiguvare. 2017. Evaluation of wind shear coefficients, surface roughness and energy yields over inland locations in Namibia. Procedia Manufacturing 7:630–38. doi:10.1016/j.promfg.2016.12.094.
   Google Scholar
• Ozay, C., and M. S. Celiktas. 2016. Statistical analysis of wind speed using two-parameter Weibull distribution in Alaçatı region. Energy Conversion and Management 121:49–54. doi:10.1016/j.enconman.2016.05.026.
   Web of Science ®Google Scholar
• Pauler, D. K. 1998. The Schwarz criterion and related methods for normal linear models. Biometrika 85:13–27. doi:10.1093/biomet/85.1.13.
   Web of Science ®Google Scholar
• Rocha, P. A. C., R. C. de Sousa, C. F. de Andrade, and M. E. V. da Silva. 2012. Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil. Applied Energy 89:395–400. doi:10.1016/j.apenergy.2011.08.003.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Seguro, J. V., and T. W. Lambert. 2000. Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis. Journal of Wind Engineering and Industrial Aerodynamics 85:75–84. doi:10.1016/S0167-6105(99)00122-1.
   Web of Science ®Google Scholar
• Shu, Z., Q. Li, and P. Chan. 2015. Statistical analysis of wind characteristics and wind energy potential in Hong Kong. Energy Conversion and Management 101:644–57.
   Web of Science ®Google Scholar
• Solyali, D., M. Altunç, S. Tolun, and Z. Aslan. 2016. Wind resource assessment of Northern Cyprus. Renewable and Sustainable Energy Reviews 55:180–87. doi:10.1016/j.rser.2015.10.123.
   Web of Science ®Google Scholar
• Spiegelhalter, D. J., N. G. Best, B. P. Carlin, and A. Van Der Linde. 2014. The deviance information criterion: 12 years on. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76:485–93. doi:10.1111/rssb.12062.
   Web of Science ®Google Scholar
• Srivastava, M. S. 1984. A measure of skewness and kurtosis and a graphical method for assessing multivariate normality. Statistics & Probability Letters 2:263–67. doi:10.1016/0167-7152(84)90062-2.
   Web of Science ®Google Scholar
• Stevens, M., and P. Smulders. 1979. The estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes. Wind Engineering 3 (2):132–45.
   Google Scholar
• Sürücü, B. 2006. Goodness-of-fit tests for multivariate distributions. Communications in Statistics-Theory and Methods 35:1319–31. doi:10.1080/03610920600628999.
   Web of Science ®Google Scholar
• Tar, K. 2008. Some statistical characteristics of monthly average wind speed at various heights. Renewable and Sustainable Energy Reviews 12:1712–24.
   Web of Science ®Google Scholar
• Teimourian, A., A. Bahrami, H. Teimourian, M. Vala, and A. Oraj Huseyniklioglu. 2020. Assessment of wind energy potential in the southeastern province of Iran. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 42:329–43. doi:10.1080/15567036.2019.1587079.
   Web of Science ®Google Scholar
• Tjur, T. 2009. Coefficients of determination in logistic regression models—A new proposal: The coefficient of discrimination. The American Statistician 63:366–72. doi:10.1198/tast.2009.08210.
   Web of Science ®Google Scholar
• Togrul, I. T., and C. Ertekin. 2011. A statistical investigation on the wind energy potential of Turkey’s geographical regions. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 33:1399–421. doi:10.1080/15567030903330843.
   Web of Science ®Google Scholar
• Tran, V.-T., and T.-H. Chen. 2016. Wind energy resources on Phuquoc island, Vietnam. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 38:1612–19. doi:10.1080/15567036.2014.940095.
   Web of Science ®Google Scholar
• Wang, J., J. Hu, and K. Ma. 2016. Wind speed probability distribution estimation and wind energy assessment. Renewable and Sustainable Energy Reviews 60:881–99. doi:10.1016/j.rser.2016.01.057.
   Web of Science ®Google Scholar