Comparison of different statistical methods used to estimate Weibull parameters for wind speed contribution in nearby an offshore site, Republic of Korea

In this article, we introduced a new approach based on graphical method (GPM), maximum likelihood method (MLM), energy pattern factor method (EPFM), empirical method of Justus (EMJ), empirical method of Lysen (EML) and moment method (MOM) using the even or odd classes of wind speed series distribution histogram with 1 m/s as bin size to estimate the Weibull parameters. This new approach is compared on the basis of the resulting mean wind speed and its standard deviation using seven reliable statistical indicators (RPE, RMSE, MAPE, MABE, R2, RRMSE and IA). The results indicate that this new approach is adequate to estimate Weibull parameters and can outperform GPM, MLM, EPF, EMJ, EML and MOM which uses all wind speed time series data collected for one period. The study has also found a linear relationship between the Weibull parameters K and C estimated by MLM, EPFM, EMJ, EML and MOM using odd or even class wind speed time series and those obtained by applying these methods to all class (both even and odd bins) wind speed time series. Another interesting feature of this approach is the data size reduction which eventually leads to a reduced processing time.Article History: Received February 16th 2018; Received in revised form May 5th 2018; Accepted May 27th 2018; Available onlineHow to Cite This Article: Salami, A.A., Ajavon, A.S.A., Kodjo, M.K. , Ouedraogo, S. and Bédja, K. (2018) The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters. Int. Journal of Renewable Energy Development 7(2), 139-150.https://doi.org/10.14710/ijred.7.2.139-150

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Statistical Analysis of Weather Effects on PM2.5

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.610-613.1033 ◽

2012 ◽

Vol 610-613 ◽

pp. 1033-1040

Author(s):

Wei Dai ◽

Jia Qi Gao ◽

Bo Wang ◽

Feng Ouyang

Keyword(s):

Statistical Analysis ◽

Wind Speed ◽

Relative Humidity ◽

Statistical Methods ◽

Predictive Analytics ◽

Hypothesis Test ◽

Meteorological Data ◽

Weather Conditions ◽

Statistical Distribution ◽

Weather Effects

Effects of weather conditions including temperature, relative humidity, wind speed, wind and direction on PM2.5 were studied using statistical methods. PM2.5 samples were collected during the summer and the winter in a suburb of Shenzhen. Then, correlations, hypothesis test and statistical distribution of PM2.5 and meteorological data were analyzed with IBM SPSS predictive analytics software. Seasonal and daily variations of PM2.5 have been found and these mainly resulted from the weather effects.

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

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d1033.1284s219 ◽

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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Wind Speed Forecasting by Conventional Statistical Methods and Machine Learning Techniques

10.1109/epec52095.2021.9621686 ◽

2021 ◽

Author(s):

Shah Mohammad Rezwanul Haque Shawon ◽

Md Abu Saaklayen ◽

Xiaodong Liang

Keyword(s):

Machine Learning ◽

Wind Speed ◽

Statistical Methods ◽

Machine Learning Techniques ◽

Wind Speed Forecasting ◽

Learning Techniques

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Comparison Of Three Numerical Methods For Estimating Weibull Parameters Using Weibull Distribution Model In Nigeria

Nigerian Journal of Basic and Applied Sciences ◽

10.4314/njbas.v27i2.2 ◽

2020 ◽

Vol 27 (2) ◽

pp. 8-15

Author(s):

J.A. Oyewole ◽

F.O. Aweda ◽

D. Oni

Keyword(s):

Standard Deviation ◽

Wind Speed ◽

Probability Density Function ◽

Weibull Distribution ◽

Probability Density ◽

Density Function ◽

Accurate Estimation ◽

Weibull Parameters ◽

Factor Method ◽

Deviation Method

There is a crucial need in Nigeria to enhance the development of wind technology in order to boost our energy supply. Adequate knowledge about the wind speed distribution becomes very essential in the establishment of Wind Energy Conversion Systems (WECS). Weibull Probability Density Function (PDF) with two parameters is widely accepted and is commonly used for modelling, characterizing and predicting wind resource and wind power, as well as assessing optimum performance of WECS. Therefore, it is paramount to precisely estimate the scale and shape parameters for all regions or sites of interest. Here, wind data from year 2000 to 2010 for four different locations (Port Harcourt, Ikeja, Kano and Jos) were analysed and the Weibull parameters was determined. The three methods employed are Mean Standard Deviation Method (MSDM), Energy Pattern Factor Method (EPFM) and Method of Moments (MOM) for estimating Weibull parameters. The method that gave the most accurate estimation of the wind speed was MSDM method, while Energy Pattern Factor Method (EPFM) is the most reliable and consistent method for estimating probability density function of wind. Keywords: Weibull Distribution, Method of Moment, Mean Standard Deviation Method, Energy Pattern Method

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