Abstract
Information on wind speed and wind power distribution is significant for a few reasons, for example, surveying wind assets, arranging wind cultivates, and limiting the liabilities for wind power improvement. This study provided an application of a new generalization of two-parameter generalized inverse Lindley distribution using the Marshall–Olkin family for analyzing wind speed and wind power characteristics. Some mathematical properties of the new distribution were studied. We had observed the suitability of new distribution as compared to the other well-known wind speed distributions such as Weibull, inverse exponential, inverted Kumaraswamy, inverse Weibull, inverse Lindley, and generalized inverse Lindley distribution. For this purpose, the time-based wind speed data is taken from the four stations of Pakistan as a case study. We conclude based on certain goodness of fit criteria that the newly developed distribution has a better fit as compared to the other wind speed distributions. Therefore, the new model can be used as an alternative distribution for the assessment of wind speed energy potential.
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References
Abbas K, Alamgir KS, Ali A, Khan DM, Khalil U (2012) Statistical analysis of wind speed data in Pakistan. World Appl Sci J 18(11):1533–1539
Akgül FG, Şenoğlu B, Arslan T (2016) An alternative distribution to Weibull for modeling the wind speed data: Inverse Weibull distribution. Energy Convers Manag 114:234–240
Alavi O, Mohammadi K, Mostafaeipour A (2016) Evaluating the suitability of wind speed probability distribution models: a case of study of east and southeast parts of Iran. Energy Convers Manag 119:101–108
Alkarni SH (2015) Extended inverse Lindley distribution: properties and application. SpringerPlus 4(1):690
Arslan T, Acitas S, Senoglu B (2017) Generalized Lindley and power Lindley distributions for modeling the wind speed data. Energy Convers Manag 152:300–311
Barco KVP, Mazucheli J, Janeiro V (2017) The inverse power Lindley distribution. Commun Stat Simul Comput 46(8):6308–6323
Dey S, Nassar M, Kumar D (2019) Alpha power transformed inverse Lindley distribution: a distribution with an upside-down bathtub-shaped hazard function. J Comput Appl Math 348:130–145
Eltehiwy M (2020) Logarithmic inverse Lindley distribution: Model, properties and applications. J King Saud Univ Sci 32(1):136–144
Ghitany ME, Atieh B, Nadarajah S (2008) Lindley distribution and its application. Math Comput Simul 78(4):493–506
Haq MA, Srinivasa-Rao G, Albassam M, Aslam M (2020b) Marshall–Olkin power lomax distribution for modeling of wind speed data. Energy Rep 6:1118–1123
Haq MA, Chand S, Sajjad MZ, Usman RM (2020a) Evaluating the suitability of two parametric wind speed distributions: a case study from Pakistan. Model Earth Syst Environ
Jung C, Schindler D (2017) Global comparison of the goodness-of-fit of wind speed distributions. Energy Convers Manag 133:216–234
Kantar YM, Usta I, Arik I, Yenilmez I (2018) Wind speed analysis using the extended generalized Lindley distribution. Renew Energy 118:1024–1030
Kassem Y, Gökçekuş H, Janbein W (2020) Predictive model and assessment of the potential for wind and solar power in Rayak region, Lebanon. Model Earth Syst Environ 1–28
Khan MA, Çamur H, Kassem Y (2019) Modeling predictive assessment of wind energy potential as a power generation sources at some selected locations in Pakistan. Model Earth Syst Environ 5(2):555–569
Lindley DV (1958) Fiducial distributions and Bayes’ theorem. J R Stat Soc Ser B (Methodol) 20:102–107
Marshall AW, Olkin I (1997) A new method for adding a parameter to a family of distributions with application to the exponential and Weibull families. Biometrika 84(3):641–652
Mohammadi K, Alavi O, McGowan JG (2017) Use of Birnbaum-Saunders distribution for estimating wind speed and wind power probability distributions: a review. Energy Convers Manag 143:109–122
Sharma VK, Singh SK, Singh U, Agiwal V (2015) The inverse Lindley distribution: a stress-strength reliability model with application to head and neck cancer data. J Ind Prod Eng 32(3):162–173
Sharma VK, Singh SK, Singh U, Merovci F (2016) The generalized inverse Lindley distribution: A new inverse statistical model for the study of upside-down bathtub data. Commun Stat Theory Methods 45(19):5709–5729
Soukissian T (2013) Use of multi-parameter distributions for offshore wind speed modeling, the Johnson SB distribution. Appl Energy 111:982–1000
Valasai GD, Uqaili MA, Memon HR, Samoo SR, Mirjat NH, Harijan K (2017) Overcoming electricity crisis in Pakistan: a review of sustainable electricity options. Renew Sustain Energy Rev 72:734–745
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Shoaib, M., Dar, I.S., Ahsan-ul-Haq, M. et al. A sustainable generalization of inverse Lindley distribution for wind speed analysis in certain regions of Pakistan. Model. Earth Syst. Environ. 8, 625–637 (2022). https://doi.org/10.1007/s40808-021-01114-7
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DOI: https://doi.org/10.1007/s40808-021-01114-7