An Improved Method for Short-Term Power Load Forecasting Based on Fractal Extrapolation

Load forecasting based on fractal extrapolation is a very important method. However, traditional methods exists several disadvantages such as vertical scale factor difficult to calculate, low-precision, difficult to use. Therefore, a method is proposed combined with self-similarity theory and fractal extrapolation theory to solve the above problems. In this paper, the self-similarity of electrical load historical data is analyzed using multi-resolution wavelet firstly. Then use the Hurst parameter values to calculate vertical scaling factors based on the values of Hurst parameter and the other four parameters of Iterative Function Systems (IFS) affine transformation. At last the electrical load forecasting curve was generated by the iterations system. Considering the actual practical application, the algorithm was used to forecast electrical load based on fractal extrapolation. The computer simulation resulted that this algorithm has advantages of high-precision, less-sample demands, less-interpolation points and easy to use.