Dynamic Gm(1,1) model based on cubic spline for electricity consumption prediction in smart grid

Electricity demand forecasting plays an important role in smart grid expansion planning. In this paper, we present a dynamic GM(1,1) model based on grey system theory and cubic spline function interpolation principle. Using piecewise polynomial interpolation thought, this model can dynamically predict the general trend of time series data. Combined with low-order polynomial, the cubic spline interpolation has smaller error, avoids the Runge phenomenon of high-order polynomial, and has better approximation effect. Meanwhile, prediction is implemented with the newest information according to the rolling and feedback mechanism and fluctuating error is controlled well to improve prediction accuracy in time-varying environment. Case study using the living electricity consumption data of Jiangsu province in 2008 is presented to demonstrate the effectiveness of the proposed model.