Energy Time Series Forecasting Based on Empirical Mode Decomposition and FRBF-AR Model - Wenquan Xu (IEEE Access, 2019)
Energy time series forecasting is challenging due to its uncertainty of natural factor. Energy time series has nonlinear and non-stationary characteristics and not normally distributed, which makes it difficult to be forecast by statistical or computational intelligent methods. A set of locally linear fuzzy radial basis function (FRBF) to approximate the functional coefficients of the state-dependent autoregressive (SD-AR) model and the proposed model is called FRBF-AR model, which combines the advantage of FRBF in function approximation and the merit of SD-AR model in nonlinear dynamics description. Empirical mode decomposition (EMD) is a powerful tool to decompose a complex time series into a number of intrinsic mode functions (IMFs) and one residual series. Then, these sub-series are predicted by the FRBF-AR model. Finally, the prediction results of the IMFs and residual series are added to formulate an ensemble forecast for the original energy time series. Furthermore, energy time series data sets from the generated electricity and load demand time series are used to test the effectiveness of the proposed hybrid modeling method. The simulation results demonstrate the effectiveness of the proposed hybrid modeling method compared with other forecasting methods.