Deakin University

File(s) under permanent embargo

Enhanced estimation of autoregressive wind power prediction model using constriction factor particle swarm optimization

conference contribution
posted on 2014-01-01, 00:00 authored by Adnan AnwarAdnan Anwar, A N Mahmood
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 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.



IEEE Industrial Electronics Chapter, Singapore. Conference (9th : 2014 : Hangzhou, China)


IEEE Industrial Electronics Chapter, Singapore Conference


1136 - 1140


Institute of Electrical and Electronics Engineers


Hangzhou, China

Place of publication

Pisctaway, N.J.

Start date


End date






Publication classification

E1.1 Full written paper - refereed

Copyright notice

2014, IEEE



Title of proceedings

ICIEA 2014 : Proceedings of the 2014 9th IEEE Conference on Industrial Electronics and Applications