Comparative Analysis of Wind Speed Models Using Different Weibull Distributions

被引:3
|
作者
Dokur, Emrah [1 ]
Ceyhan, Salim [2 ]
Kurban, Mehmet [1 ]
机构
[1] Bilecik Seyh Edebali Univ, Dept Elect Elect Engn, Bilecik, Turkey
[2] Bilecik Seyh Edebali Univ, Dept Comp Engn, Bilecik, Turkey
来源
ELECTRICA | 2019年 / 19卷 / 01期
关键词
Wind speed modeling; weibull distribution; rayleigh distribution; inverse weibull distribution; PROBABILITY-DISTRIBUTIONS; PARAMETERS;
D O I
10.26650/electrica.2018.28091
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A wide variety of distribution functions are used in the literature for wind speed modelling. It is the most widely used Weibull distribution (WD) function in wind speed modelling. In this paper, two-parameter WD, Rayleigh distribution (RD) which is a special form of WD, and Inverse Weibull distribution (IWD) offered for a new seasonal wind speed modelling are considered and analyzed for six different regions (Gokceada, Bozcaada, Bandirma, Bilecik, Yalova and Sakarya regions) in the Northwest of Turkey, comparatively. The hourly wind speed data for the period of October 2015 to 30 September 2016 is taken from Turkish State Meteorological Service. As a result of the comparison, it is seen that the WD is generally suitable, although IWD has good seasonal results in some regions. All the comparative results are given in tables.
引用
收藏
页码:22 / 28
页数:7
相关论文
共 50 条
  • [41] SDE-based Wind Speed Models with Weibull Distribution and Exponential Autocorrelation
    Zarate-Minano, Rafael
    Mele, Francesca Madia
    Milano, Federico
    [J]. 2016 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PESGM), 2016,
  • [42] Comparison of Different Models for Wind Speed Prediction
    Lazarevska, Elizabeta
    [J]. PROCEEDINGS OF THE IECON 2016 - 42ND ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2016, : 5544 - 5549
  • [43] Statistical analysis of wind speed using two-parameter Weibull distribution in Alacati region
    Ozay, Can
    Celiktas, Melih Soner
    [J]. ENERGY CONVERSION AND MANAGEMENT, 2016, 121 : 49 - 54
  • [44] A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed
    Elsaraiti, Meftah
    Merabet, Adel
    [J]. ENERGIES, 2021, 14 (20)
  • [45] Comparative analysis of regression and artificial neural network models for wind speed prediction
    Bilgili, Mehmet
    Sahin, Besir
    [J]. METEOROLOGY AND ATMOSPHERIC PHYSICS, 2010, 109 (1-2) : 61 - 72
  • [46] Comparative analysis of regression and artificial neural network models for wind speed prediction
    Mehmet Bilgili
    Besir Sahin
    [J]. Meteorology and Atmospheric Physics, 2010, 109 : 61 - 72
  • [47] Wind Speed Data and Wind Energy Potential Using Weibull Distribution in Zagora, Morocco
    Mohammed, Daoudi
    Abdelaziz, Ait Sidi Mou
    Mohammed, Elkhomri
    Elmostapha, Elkhouzai
    [J]. INTERNATIONAL JOURNAL OF RENEWABLE ENERGY DEVELOPMENT-IJRED, 2019, 8 (03): : 267 - 272
  • [48] Failure data analysis by models involving 3 Weibull distributions
    Zhang, TL
    Ren, YK
    [J]. ANNUAL RELIABILITY AND MAINTAINABILITY SYMPOSIUM, 2002 PROCEEDINGS, 2002, : 44 - 50
  • [49] THE CHARACTERISTICS OF WIND VELOCITY THAT FAVOR THE FITTING OF A WEIBULL DISTRIBUTION IN WIND-SPEED ANALYSIS
    TULLER, SE
    BRETT, AC
    [J]. JOURNAL OF CLIMATE AND APPLIED METEOROLOGY, 1984, 23 (01): : 124 - 134
  • [50] Confidence intervals for the difference between the coefficients of variation of Weibull distributions for analyzing wind speed dispersion
    La-ongkaew, Manussaya
    Niwitpong, Sa-Aat
    Niwitpong, Suparat
    [J]. PEERJ, 2021, 9