Application of Improved Grey Prediction Model to Short Term Load Forecasting

The grey model GM(1,1) based on the grey system theory has recently emerged as a powerful tool for short term load forecasting (STLF) problem. Since GM(1,1) is only first order dynamic grey model, the accuracy is not satisfactory when original data show great randomness. In this paper, we proposed improved dynamic mode GM(2,1) to enhance forecasted accuracy. Then it is applied to improve STLF performance. The improved procedure is shown as follows briefly: We presented a grey interval analysis, then this analysis based whitening coefficients were presented. Furthermore, these coefficients were combined with cubic spline function to establish GM(2,1) model. Finally, Taylor approximation method is presented to optimize these whitening coefficients and make forecasted error reduce to minimum. The improved GM(2,1) model is defined as T-3spGM(2,1) and it can overcome above mentioned shortcomings. The power system load data of ordinary and special days were used to test the proposed model. The experimental results showed that the proposed T-3spGM(2,1) model has better performance for STLF problem.