dc.description.abstract | Renewable energy has gained momentum over the last decade, with wind power
technology leading by the number of projects implemented worldwide and
academia as research[1]. Worldwide the growth of wind energy generation[2] has
increased tremendously over the last 10 years, for instance, in 2000, installed
capacity was 60GW and in 2010 it was 160GW. Furthermore, by 2015 the total
global installation to 433GW.
With that kind of growth, energy sector professionals have had to keep pace
finding better, reliable and efficient ways in the management of wind power farms
and turbine designs that are cost effective. Numerical Weather Prediction
software’s (NWP) systems and meteorological tools are too expensive to acquire
and maintain, therefore, these constraints have necessitated the development of
accurate prediction system that are simple, fast and cheaper that can be used by
system planners, power regulatory experts and the academic community through
research.
Short term wind speed to wind-power predicting model is the main object for this
research , the model will be optimized using a hybrid of particle swam
optimization (PSO) and neural networks. Matlab modeling environment has been
used extensively in this research work.
The hybrid prediction model performance will be evaluated, see chapter 4 and
obtained the following values, using the mean square errors (MSE) of 0.26, the
mean average error(MAE) of 0.62 and the mean average percentage error(MAPE)
of 18.2. | en_US |