scholarly journals Estimate wind speed in Ninewa province using Weppel parameters: تخمين سرع الرياح في محافظة نينوى باستخدام معلمات ويبل

2017 ◽  
Vol 1 (4) ◽  
Author(s):  
Waleed Asmair Al Rajbo, Faten Mohammed Hamam
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Coefficient Of Determination ◽  
Weibull Parameter ◽  
Energy Investment ◽  
Weibull Parameters ◽  
The Mean ◽  
Very High

The study and forecasting of wind speed is of great importance in weather phenomena, climate and wind energy investment for electricity and other uses. The aim of this paper is to estimate the mean monthly values of wind speed in five  Meteorological stations in Ninava Governorate (Mosul, Rabea ,Sin jar ,Talafar ,Baag) using weibull parameter . The estimated mean monthly values of wind speed using weibull parameters in all stations are nearly equal to the measured values. The coefficient of determination (R2 ) between the measured and estimated values in all stations are (0.997, 0.956, 0.995, 0.997, 0.994) . R2 between the measured and estimated values of wind speed in the whole Ninava Governorate is gives a very high value equal to ( 0.998) . This mean that the model is very accurate.

scholarly journals Characterization of Diameter Distribution and Prediction of Weibull Parameters Equation for Plantation-grown Eucalyptus Species

2021 ◽  
pp. 1-13
Author(s):  
M. G. Saka
Keyword(s):  
Coefficient Of Determination ◽  
Diameter Distribution ◽  
Eucalyptus Species ◽  
Weibull Parameter ◽  
Weibull Parameters ◽  
Breast Height ◽  
The Mean ◽  
Age Series ◽  
Stand Characteristics

Aim: To characterization of the diameter distribution and prediction of Weibull parameters of a plantation-grown Eucalyptus species. Study design: Stratified sampling method was adopted, in which the plantation was stratified into four age series. Place and duration of study: Afaka Forest Reserve, one month. Methodology: Fifty (50) sample plots of 20 x 20 m were laid across the age series. In each of the plot, all the trees were counted and data of variable of interest was collected and processed. A separate Weibull distribution is fitted to the diameter at breast height (dbh) frequency data from each of the plot for the estimation of Weibull parameters (location, scale and shape). The data set obtained from the Weibull parameter estimate was then used to develop regression equations with the stand variables. Coefficient of Determination (R2) and Root Mean Square Error (RMSE) was used as goodness of fit test. Results: The result on the stand characteristics revealed that, the mean diameter at breast height (dbh) ranges between 13.4 – 18.2 cm across the four stands. This indicates that the species are still of pole sizes. The average site productivity of the species ranges between 24.0 m to 37 m at an index of 25 years. The mean Basal area varies between 14.13 to 26.85 m2 per ha, while the average tree total height ranges between 24.6 to 28.2 m across the four species. The result on diameter class distribution shows that most of the species fell within dbh class of 11 -20 cm class except E. cloeziana in which the highest frequency fell into 16 – 20cm dbh class. Best equation were selected for each of the Weibull parameters (α, β, ) per species based on fit statistics. The formation of straight line pattern from the plotted normal probability plots indicates the adequacy of the selected models for predicting Weibull parameters. A fluctuation pattern exists between the Weibull parameters and the stand characteristics. this may be due to variation in climatic factor, most especially fluctuations in rainfall pattern in the area at that particular period. Conclusion: The ease of fit and high value of coefficient of determination of the models in this study has re-affirmed the use of Weibull parameter in prediction of stand characteristics as been suggested by many authors in the literature.

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

Wind Speed Change and Influence on Wind Energy in Recent 50 Years over Hexi Corridor

2014 ◽  
Vol 1070-1072 ◽  
pp. 284-290
Author(s):  
Yao Zong Zhang ◽  
Bo Zhang ◽  
Yan Yan Liu
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Nonparametric Test ◽  
Test Methods ◽  
Hexi Corridor ◽  
Mean Wind Speed ◽  
Seasonal Wind ◽  
Speed Change ◽  
The Mean ◽  
Different Characteristics

Based on wind speed data of 13 meteorological stations in 1958-2012,Mann-Kendall nonparametric test methods was been used to study on wind speed changes in Hexi Corridor.Spatial and temporal characteristics of seasonal and monthly wind speed changes was examined. (1) The maximum wind speed appeared in the higher elevations of study area, such as Wushaoling and Mazongshan station. From east to west mean wind speed increased in Hexi Corridor.For nearly 50 years wind speed had showed decreasing trend. (2)In each season Spring with an maximum mean wind speed was 3.4m/s,the Summer mean wind speed was 2.9 m/s,Autumn mean wind speed was 2.6 m/s,the mean Winter wind speed was 2.8m/s.The seasonal wind speed mainly had decline trend, each station.has different characteristics trends (3) Mean wind speed in each month was greater than 2.5m/s,maximum monthly wind speed appeared in April was 3.5m/s,the minimum wind speed appeared in the September-October was 2.53m/s,the wind speed in March,April and May was greater than the November month,December,January.In addition to Mazongshan and Wushaoling,other station monthly wind speed showed a decreasing trend.Monthly mean wind speed in Jiuquan,Dingxin and Zhangye was slow decreasing trend.Anxi,Yumen wind decreasing trend were more obvious.(4)Wind decreasing trend will have a significant impact on wind energy, wind speed changes and wind energy should be evaluated in the future.

scholarly journals Wind Energy Assessment at Bafoussam, Cameroon

2015 ◽  
Vol 8 (9) ◽  
pp. 106 ◽  
Author(s):  
Talla Pierre Kisito ◽  
Bawe Gerard Nfor, Jr ◽  
Yemele David ◽  
Ghogomu Patrick Ndinakie
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Power Density ◽  
Goodness Of Fit ◽  
Energy Resource ◽  
Coefficient Of Determination ◽  
Wind Rose ◽  
Chi Square ◽  
Wind Speeds ◽  
Goodness Of Fit Tests

<p>Three-hourly wind speed data measured using the Beaufort scale at a height of 10m, from 6am to 6pm local time (5 periods per day), was obtained from the Bafoussam Airport. It was analyzed using the Weibull and Rayleigh probability density models and wind rose plots. It was determined that the lowest wind speeds (most calms) were observed during the first period (6am) and the highest at 3pm (fourth period). The very low morning wind speed adversely affected the daily mean wind speed. Better, but still poor, power density results were obtained at this fourth (3pm) period. The monthly and yearly mean speeds varied between 1.9 and 3.1m/s and with very low standard deviations. The wind rose plots also showed that all the significant winds fell in the first quadrant (NE) and predominantly on angle 10<sup>o</sup> with some discernibly on 20<sup>o</sup> and 30<sup>o</sup>, only. Three goodness-of-fit tests: the chi square, coefficient of determination or R<sup>2</sup> and root mean square error, showed the Weibull to be a better fit to the wind regime than the Rayleigh model. The shape parameters were always greater than the scale parameters. Results show that, using the Weibull parameters, the power density of Bafoussam falls in the category 1 of the wind energy resource group and hence is not a very good wind energy exploitable candidate.</p>

scholarly journals Estimation of Weibull parameters in wind speed mixture using nonlinear optimization for wind energy applications

2020 ◽  
Vol 18 ◽  
pp. 351-355
Author(s):  
Francisco M. Arrabal-Campos ◽  
◽  
Francisco G. Montoya ◽  
Alfredo Alcayde ◽  
Raúl Baños ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Nonlinear Optimization ◽  
Weibull Parameters ◽  
Energy Applications

scholarly journals Statistical wind speed studies and wind energy potential resource analysis of abong mbang, Cameroon: a case study

2015 ◽  
Vol 4 (4) ◽  
pp. 466
Author(s):  
Yemele David ◽  
Bawe Gerard Nfor, Jr ◽  
Talla Pierre Kisito ◽  
Ghogomu Patrick Ndinakie
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Turbine ◽  
Goodness Of Fit ◽  
Energy Resource ◽  
Coefficient Of Determination ◽  
Energy Potential ◽  
Accurate Analysis ◽  
Chi Square ◽  
Wind Energy Potential

<p>Accurate analysis of wind characteristics for a particular site is the first step towards wind energy resource installation. In this study, the onus is to determine the wind energy potential characteristics, and the best representative probability density function, for the Abong Mbang weather station and its immediate environ. The Chi square, coefficient of determination and root mean square error were used as the discriminating goodness of fit tests. Results show that the gamma distribution is the best representative of the wind speed regime, closely followed by the Weibull distribution. We equally study the feasibility of the installation of wind turbine systems at this site based on the Weibull and the Rayleigh models. It is observed that Abong Mbang is characterized by very low wind speeds, higher shape parameters than the scale parameters and consequently very low power density values. Abong Mbang is not technically feasible for the installation of small wind turbine.</p>

Comparison of Three Methods for Weibull Distribution in Calculating Wind Energy Potential

2014 ◽  
Vol 492 ◽  
pp. 574-578 ◽  
Author(s):  
Razika Ihaddadene ◽  
Nabila Ihaddadene ◽  
Merouane Mostefaoui
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Weibull Distribution ◽  
Failure Analysis ◽  
Power Density ◽  
Moment Method ◽  
Ground Level ◽  
Coefficient Of Determination ◽  
Energy Potential ◽  
The Moment

Three kinds of methods commonly used for estimating Weibull parameters were fitted to a collection of wind speed data at 10 m above ground level for the year of 2009 to determine the best distribution function which describes the wind speed variation at Msila, Algeria site for wind energy. Three methods used the coefficient of determination R2, root mean square error RMSE and Chi-Square χ2 were compared with failure analysis. According to the results of failure analysis the moment method has better results than graphic method and power density method. The wind power density calculated from moment method shows a good approximation to estimate the power density. So the Weibull distribution using the moment method adequately fit the data and it is suitable for modeling the wind speed distribution in Msila province of Algeria.

scholarly journals Evaluation of wind energy investment with artificial neural networks

2019 ◽  
Vol 9 (2) ◽  
pp. 142-147
Author(s):  
Hasan Huseyin Yildirim ◽  
Mehmet Yavuz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Renewable Energy ◽  
Wind Speed ◽  
Wind Energy ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Energy Investment ◽  
Data Set

Countries aiming for sustainability in economic growth and development ensure the reliability of energy supplies. For countries to provide their energy needs uninterruptedly, it is important for domestic and renewable energy sources to be utilised. For this reason, the supply of reliable and sustainable energy has become an important issue that concerns and occupies mankind. Of the renewable energy sources, wind energy is a clean, reliable and inexhaustible source of energy with low operating costs. Turkey is a rich nation in terms of wind energy potential. Forecasting of investment efficiency is an important issue before and during the investment period in wind energy investment process because of high investment costs. It is aimed to forecast the wind energy products monthly with multilayer neural network approach in this study. For this aim a feed forward back propagation neural network model has been established. As a set of data, wind speed values 48 months (January 2012-December 2015) have been used. The training data set occurs from 36 monthly wind speed values (January 2012-December 2014) and the test data set occurs from other values (January-December 2015). Analysis findings show that the trained Artificial Neural Networks (ANNs) have the ability of accurate prediction for the samples that are not used at training phase. The prediction errors for the wind energy plantation values are ranged between 0.00494-0.015035. Also the overall mean prediction error for this prediction is calculated as 0.004818 (0.48%). In general, we can say that ANNs be able to estimate the aspect of wind energy plant productions.

scholarly journals Performance Comparison of New Generation Parameter Estimation Methods for Weibull Distribution to Compute Wind Energy Density

2021 ◽  
Author(s):  
Ahmet Emre Onay ◽  
Emrah Dokur ◽  
Mehmet Kurban
Keyword(s):  
Parameter Estimation ◽  
Wind Speed ◽  
Wind Energy ◽  
Weibull Distribution ◽  
Performance Metrics ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Coefficient Of Determination ◽  
Wind Speed Data ◽  
Parameter Estimation Methods

To install a wind energy conversion system to a region, the wind speed characteristics of that region must be identified. The two-parameter Weibull distribution is highly efficient in modeling wind speed characteristics. In this study, the wind speed data of 32 cities in three different regions of Turkey have been comparatively analysed to estimate Weibull distribution function parameters by the use of three well-known methods (Graphical Method (GM), Maximum Likelihood Method (MLM), Justus Moment Method (JMM)) and three new parameter estimation methods (Energy Pattern Factor Method (EPFM), Wind Energy Intensification Method (WEIM), Power Density Method (PD)) which have been proposed in recent years. Three years of hourly wind speed data of the specified regions have been used. The performance metrics of these analyses have been compared using Wind Energy Error (WEE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2). The results have shown that while the PD method has high model performance, the JMM is closely competitive with the MLM. Besides, the wind energy densities that were estimated by using actual data have been compared with the resulting Weibull distribution. It has been clear that the method that has the closest estimation to the actual values is the PD method.

scholarly journals Prospects of Wind Energy for Power Generation in University of Benin

2016 ◽  
Vol 13 (1) ◽  
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Energy ◽  
Energy Resource ◽  
Maximum Energy ◽  
Mean Power ◽  
Energy And Environment ◽  
National Centre ◽  
Mean Wind Speed ◽  
The Mean

Wind is a renewable energy resource by nature. It is clean, abundant, inexhaustible and environmentally friendly. Essentially, this study investigated the prospects of wind energy for power generation in University of Benin. Wind data from Jan 31st – Dec 31st 2013 (at 10m height) was collected from National Centre for Energy and Environment, University of Benin. Accordingly, the annual and monthly wind speed and density are estimated using the 2- parameter Weibull probability density function. From the analysis, results obtained shows that the highest mean wind speed of 1.975m/s occurred in March and the lowest monthly mean speed of 0.977m/s occurred in November. Also, the annual mean wind speed is 1.496m/s while the annual mean power density based on Weibull distribution function is 2.692W/m2 . Further results shows that the mean annual most probable wind speed and wind speed carrying maximum energy are 1.535m/s and 1.761m/s respectively. Thus, it is recommended that the institution can tap on the available wind power potential to augment its power supply.

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