Enhanced Estimation of Autoregressive Wind Power Prediction Model Using Constriction Factor Particle Swarm Optimization

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

The smooth transition autoregressive target zone model with the Gaussian stochastic volatility and TGARCH error terms with applications

This paper proposes to model the error term in smooth transition autoregressive target zone model as Gaussian with stochastic volatility (STARTZ-SV) or as Student-t with GARCH volatility (STARTZ-TGARCH). Using the dynamics of Norwegian krone exchange rate index, we show that both models produce standardized residuals that are closer to assumed distributions and do not produce a hump in the estimated marginal distribution of exchange rate which is more consistent with theoretical predictions. We apply developed models to test whether the dynamics of oil price can be well approximated by the Krugman’s target zone model. Our estimates of conditional volatility and marginal distribution reject the target zone hypothesis.target zone, oil price, exchange rate, stochastic volatility, griddy Gibbs, smooth transition