## Latest News on Wind Speed : April 21

**[1] Methods for Estimating Wind Speed Frequency Distributions**

The Weibull function is discussed for representation of the wind speed frequency distribution. Methods are presented for estimating the two Weibull parameters (scale factor c and shape factor k) from simple wind statistics. Comparison is made with a recently proposed method based on the “square-root-normal” distribution with mean wind speed and fastest mile data as input statistics. The Weibull distribution is shown to give smaller root-mean-square errors than the square-root-normal distribution when fitting actual distributions of observed wind speed. Another advantage of the Weibull distribution is the available methodology for projecting to another height the observed Weibull distribution parameters at anemometer height.

**[2] A review on the forecasting of wind speed and generated power**

In the world, wind power is rapidly becoming a generation technology of significance. Unpredictability and variability of wind power generation is one of the fundamental difficulties faced by power system operators. Good forecasting tools are urgent needed under the relevant issues associated with the integration of wind energy into the power system. This paper gives a bibliographical survey on the general background of research and developments in the fields of wind speed and wind power forecasting. Based on the assessment of wind power forecasting models, further direction for additional research and application is proposed.

**[3] Support vector machines for wind speed prediction**

This paper introduces support vector machines (SVM), the latest neural network algorithm, to wind speed prediction and compares their performance with the multilayer perceptron (MLP) neural networks. Mean daily wind speed data from Madina city, Saudi Arabia, is used for building and testing both models. Results indicate that SVM compare favorably with the MLP model based on the root mean square errors between the actual and the predicted data. These results are confirmed for a system with order 1 to system with order 11.

**[4] Existence and Solution of Wind Speed Equation of a Point in Air**

This paper proves the existence of solution of wind speed equation of a point in air by fixed point theorem of periodic g-contrastive mapping. Further more, the solution of the wind speed equation is obtained by method of separating variables.

**[5] A Statistical Approach to Estimate Wind Speed Distribution in Ibadan, Nigeria**

statistically analyzed using daily wind speed data for 10 years (1995-2004) obtained from the International Institute of Tropical Agriculture (IITA) and 1 year (2006) obtained from Nigeria Micro-scale Experimental (NIMEX) Ibadan, Nigeria. The statistical wind data set was analyzed using Weibull distributions in order to investigate the Weibull shape and scale parameters. The daily, monthly, seasonal, and yearly wind speed probability density distributions were modeled using Weibull Distribution Function. The measured annual mean wind speed was found to be 0.76 m/s and the total extractable wind power has been estimated as 0.33 kW at IITA while the annual mean wind speed ranged between 0.74 m/s, 1.02 m/s, 1.16 m/s and 1.34 m/s at (3 m, 6 m, 12 m and 15 m) respectively at NIMEX. The maximum extractable annual wind power density value of 0.90W / m2 for the whole year at IITA and 5.61W / m2 at the highest height of 15 m at NIMEX indicated that, Ibadan can be classified as a low wind energy region and it belongs to the wind power class 1, since the density is less than 100W / m2 . It is concluded that at both sites, the highest wind speed that prevailed in Ibadan is March and the location can be explored for wind power.

**Reference**

**[1] **Justus, C.G., Hargraves, W.R., Mikhail, A. and Graber, D., 1978. Methods for estimating wind speed frequency distributions. *Journal of applied meteorology*, *17*(3), pp.350-353.

**[2] **Lei, M., Shiyan, L., Chuanwen, J., Hongling, L. and Yan, Z., 2009. A review on the forecasting of wind speed and generated power. *Renewable and Sustainable Energy Reviews*, *13*(4), pp.915-920.

**[3] **Mohandes, M.A., Halawani, T.O., Rehman, S. and Hussain, A.A., 2004. Support vector machines for wind speed prediction. *Renewable energy*, *29*(6), pp.939-947.

**[4] **Yun, T.Q., 2016. Existence and solution of wind speed equation of a point in air. *Journal of Advances in Mathematics and Computer Science*, pp.1-6.

**[5] **Rauff, K.O. and Nymphas, E.F., 2016. A statistical approach to estimate wind speed distribution in Ibadan, Nigeria. *Physical Science International Journal*, pp.1-14.