Sensitivity analysis of different wind speed distribution models with actual and truncated wind data: A case study for Kerman, Iran

2016 ◽  
Vol 120 ◽  
pp. 51-61 ◽  
Author(s):  
Omid Alavi ◽  
Ahmad Sedaghat ◽  
Ali Mostafaeipour
Keyword(s):  
Sensitivity Analysis ◽  
Wind Speed ◽  
Wind Data ◽  
Distribution Models ◽  
Speed Distribution ◽  
Wind Speed Distribution

Deep assessment of wind speed distribution models: A case study of four sites in Algeria

2018 ◽  
Vol 155 ◽  
pp. 78-90 ◽  
Author(s):  
Nawel Aries ◽  
Sidi Mohammed Boudia ◽  
Houdayfa Ounis
Keyword(s):  
Wind Speed ◽  
Distribution Models ◽  
Speed Distribution ◽  
Wind Speed Distribution

scholarly journals Wind Speed Distributions Used in Wind Energy Assessment: A Review

2021 ◽  
Vol 9 ◽  
Author(s):  
Huanyu Shi ◽  
Zhibao Dong ◽  
Nan Xiao ◽  
Qinni Huang
Keyword(s):  
Renewable Energy ◽  
Wind Speed ◽  
Wind Energy ◽  
Estimation Methods ◽  
Model Parameters ◽  
Distribution Models ◽  
Advantages And Disadvantages ◽  
Speed Distribution ◽  
Energy Assessment ◽  
Wind Speed Distribution

With economic development and population growth, energy demand has shown an upward trend. Renewable energy is inexhaustible and causes little pollution, which has broad prospects for development. In recent years, wind energy has been developed as an essential renewable energy source. The use of wind power is very environmentally friendly and plays a critical role in economic growth. Assessing the characteristics and potential of wind energy is the first step in the effective development of wind energy. The wind speed distribution at a specific location determines the available wind energy. This paper reviews the wind speed distribution models used for wind energy assessment, and they are applicable to different wind regimes. All potential wind speed distribution models should be considered for modeling wind speed data at a particular site. Previous studies have selected several parameter estimation methods and evaluation criteria to estimate model parameters and evaluate the goodness-of-fit. This paper discusses their advantages and disadvantages. The characteristics of wind speed distribution are constantly varying geographically and temporally. Wind energy assessment should consider local geographical elements, such as local climate, topography, and thermal properties difference between the land and the sea, and focus on long-term variations in wind characteristics.

An Analysis of Wind Speed Distribution at Benina, Benghazi, Libya

2014 ◽  
Vol 492 ◽  
pp. 550-555
Author(s):  
Mohamed Salem Elmnefi ◽  
Ahmed Mohamed Bofares
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Sea Level ◽  
Ground Level ◽  
Wind Data ◽  
Data Set ◽  
Mean Sea Level ◽  
Scale Parameters ◽  
Speed Distribution ◽  
Wind Speed Distribution

The statistical wind data obtained from measurements for the 12 month period of January to December 2008 at Benina, Benghazi, Libya. The site coordinates are: latitude 32,05N and longitude 20,13E. The elevation of the site is 136 m above mean sea level (AMSL). The wind speed has been measured at height of 10 m above the ground level using 3 cup anemometers. Moreover wind speed has been estimated at height of 40 m. The statistical wind data set was analyzed using weibull distributions in order to investigate the weibull shape and scale parameters at 10 m and 40 m height. Finally, the yearly power density has been estimated at both heights. The results showed that strong and sufficient winds for power generation are available at most of months in Benina region.

scholarly journals A statistical analysis of wind speed distribution models in the Aburrá Valley, Colombia

2014 ◽  
Vol 5 (5) ◽  
pp. 121-136 ◽  
Author(s):  
Paula-Andrea Amaya-Martínez, ◽  
Andrés-Julián Saavedra-Montes ◽  
Eliana-Isabel Arango-Zuluaga
Keyword(s):  
Statistical Analysis ◽  
Wind Speed ◽  
Distribution Models ◽  
Speed Distribution ◽  
Wind Speed Distribution

scholarly journals Weibull Wind-Speed Distribution Parameters Derived from a Combination of Wind-Lidar and Tall-Mast Measurements Over Land, Coastal and Marine Sites

2015 ◽  
Vol 159 (2) ◽  
pp. 329-348 ◽  
Author(s):  
Sven-Erik Gryning ◽  
Rogier Floors ◽  
Alfredo Peña ◽  
Ekaterina Batchvarova ◽  
Burghard Brümmer
Keyword(s):  
Wind Speed ◽  
Speed Distribution ◽  
Wind Speed Distribution ◽  
Distribution Parameters ◽  
Wind Lidar

Comparison of Three Methods to Estimate Wind Speed Distribution

2011 ◽  
Author(s):  
D. K. Kirova ◽  
Michail D. Todorov ◽  
Christo I. Christov
Keyword(s):  
Wind Speed ◽  
Speed Distribution ◽  
Wind Speed Distribution

scholarly journals Artificial Intelligence Techniques for Estimation of Optimum Weibull Parameters for Wind Speed Distribution

2019 ◽  
Vol 8 (4S2) ◽  
pp. 150-156
Keyword(s):  
Wind Speed ◽  
Tamil Nadu ◽  
Optimization Algorithms ◽  
Coefficient Of Determination ◽  
Short Term ◽  
Weibull Parameters ◽  
Wind Speed Data ◽  
Speed Distribution ◽  
Wind Speed Distribution

The main objective of this study is to estimate the optimum Weibull scale and shape parameters for wind speed distribution at three stations of the state of Tamil Nadu, India using Nelder-Mead, Broyden–Fletcher–Goldfarb–Shanno, and Simulated annealing optimization algorithms. An attempt has been made for the first time to apply these optimization algorithms to determine the optimum parameters. The study was conducted for long term wind speed data (38 years), short term wind speed data (5 years) and also with single year’s wind speed data to assess the performance of the algorithm for different quantum of data. The efficiency of these algorithms are analyzed using various statistical indicators like Root mean square error (RMSE), Correlation coefficient (R), Mean absolute error (MAE) and coefficient of determination (R2). The results suggest that the performance of three algorithms is similar irrespective of the quantum of the dataset. The estimated Weibull parameters are almost similar for short term and long term dataset. There is a marginal variation in the obtained parameters when only single year’s wind data is considered for the analysis. The Weibull probability distribution curve fits very well on the wind speed histogram when only single year’s wind speed data is considered and fits marginally well when short term and long term wind speed data is considered

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