Mixture distribution; Unimodal distribution; Weibull distribution; Wind energy; Wind speed; Case-studies; Continuous probability distribution; Mixture distributions; Probability: distributions; Scientific articles; Wind energy potential; Wind power generation; Wind speed data; Environmental Engineering; Environmental Chemistry; Water Science and Technology; Safety, Risk, Reliability and Quality; Environmental Science (all)
Abstract :
[en] Modeling wind speed data is the prime requirement for harnessing the wind energy potential at a given site. While the Weibull distribution is the most commonly employed distribution in the literature and in practice, numerous scientific articles have proposed various alternative continuous probability distributions to model the wind speed at their convenient sites. Fitting the best distribution model to the data enables the practitioners to estimate the wind power density more accurately, which is required for wind power generation. In this paper we comprehensively review fourteen continuous probability distributions, and investigate their fitting capacities at seventeen locations of India covering the east and west offshore corner as well as the mainland, which represents a large variety of climatological scenarios. A first main finding is that wind speed varies a lot inside India and that one should treat each site individually for optimizing wind power generation. A second finding is that the wide acceptance of the Weibull distribution should at least be questioned, as it struggles to represent wind regimes with heterogeneous data sets exhibiting multimodality, high levels of skewness and/or kurtosis. Our study reveals that mixture distributions are very good alternative candidates that can model difficult shapes and yet do not require too many parameters.
Disciplines :
Mathematics
Author, co-author :
Gugliani, Gaurav Kumar; Department of Mechanical Engineering, Mandsaur University, Mandsaur, India
LEY, Christophe ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH)
Nakhaei Rad, Najmeh; Department of Statistics, University of Pretoria, Pretoria, South Africa ; DSI-NRF Centre of Excellence in Mathematical and Statistical Sciences (CoE-MaSS), Johannesburg, South Africa
Bekker, Andriette; Department of Statistics, University of Pretoria, Pretoria, South Africa ; DSI-NRF Centre of Excellence in Mathematical and Statistical Sciences (CoE-MaSS), Johannesburg, South Africa
External co-authors :
yes
Language :
English
Title :
Comparison of probability distributions used for harnessing the wind energy potential: a case study from India
Publication date :
June 2024
Journal title :
Stochastic Environmental Research and Risk Assessment
ISSN :
1436-3240
eISSN :
1436-3259
Publisher :
Springer Science and Business Media Deutschland GmbH
The first author is thankful to Prof. Arnab Sarkar from IIT BHU for motivating him to conduct research in climatology. We would like to thank the Indian Institute of Technology for providing the facilities, the Indian Meteorological Department (IMD), Pune, for supplying the data used in this research, and the Bhabha Atomic Research Center (BARC), Mumbai, for providing the necessary funds to carry out the reported research work. However, the opinions expressed in this manuscript are those of the authors and not of these agencies. The research of the third and fourth authors is supported in part by\u00A0the\u00A0RDP grant at university of Pretoria, the National Research Foundation (NRF) of South Africa, Ref.: RA210106581084, grant No. 150170; ref. SRUG2204203865, RA171022270376 (Grant No: 119109), the South African DST-NRF-MRC SARChI Research Chair in Biostatistics (Grant No. 114613) and DSI-NRF Centre of Excellence in Mathematical and Statistical Sciences (CoE-MaSS), South Africa. The opinions expressed and conclusions arrived at are those of the authors and are not necessarily to be attributed to the CoE-MaSS or the NRF.The first author is thankful to Prof. Arnab Sarkar from IIT BHU for motivating him to conduct research in climatology. We would like to thank the Indian Institute of Technology for providing the facilities, the Indian Meteorological Department (IMD), Pune, for supplying the data used in this research, and the Bhabha Atomic Research Center (BARC), Mumbai, for providing the necessary funds to carry out the reported research work. However, the opinions expressed in this manuscript are those of the authors and not of these agencies. The research of the third and fourth authors is supported in part by the RDP grant at university of Pretoria, the National Research Foundation (NRF) of South Africa, Ref.: RA210106581084, grant No. 150170; ref. SRUG2204203865, RA171022270376 ( Grant No: 119109), the South African DST-NRF-MRC SARChI Research Chair in Biostatistics (Grant No. 114613) and DSI-NRF Centre of Excellence in Mathematical and Statistical Sciences (CoE-MaSS), South Africa. The opinions expressed and conclusions arrived at are those of the authors and are not necessarily to be attributed to the CoE-MaSS or the NRF.
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