Accurate forecasting is important for cost-effective and efficient monitoring
and control of the renewable energy based power generation. Wind based power is
one of the most difficult energy to predict accurately, due to the widely
varying and unpredictable nature of wind energy. Although Autoregressive (AR)
techniques have been widely used to create wind power models, they have shown
limited accuracy in forecasting, as well as difficulty in determining the
correct parameters for an optimized AR model. In this paper, Constriction
Factor Particle Swarm Optimization (CF-PSO) is employed to optimally determine
the parameters of an Autoregressive (AR) model for accurate prediction of the
wind power output behaviour. Appropriate lag order of the proposed model is
selected based on Akaike information criterion. The performance of the proposed
PSO based AR model is compared with four well-established approaches;
Forward-backward approach, Geometric lattice approach, Least-squares approach
and Yule-Walker approach, that are widely used for error minimization of the AR
model. To validate the proposed approach, real-life wind power data of
\textit{Capital Wind Farm} was obtained from Australian Energy Market Operator.
Experimental evaluation based on a number of different datasets demonstrate
that the performance of the AR model is significantly improved compared with
benchmark methods.Comment: The 9th IEEE Conference on Industrial Electronics and Applications
(ICIEA) 201
