Simulation of orthogonalization prediction based on grey Markov Chain for electricity consumption

The general trend of time series data was predicted with Gauss-Chebyshev orthogonalization theory according to the grey orthogonalization method and the Markov Chain theory. The prediction accuracy is time-varying. However, this approach will better solve the problem of unstable prediction result since Markov chain theory has greater advantages in handling time-varying system process. Based on this, the Markov grey orthogonalization model prediction was proposed for electricity consumption. It is suitable for dynamic process prediction in medium and short term with less data demand and large data fluctuations. Finally, the proposed approach was used to forecast the industrial electricity consumption of Jiangsu Province in 2007, and the results show the effectiveness of this approach.