A Hybrid Forecasting Model Based on EMD-GASVM-RBFNN for Power Grid Investment Demand

Abstract

Power grid as an important infrastructure which ensures the healthy development of economy and society and accurate and reasonable prediction of the power grid investment demand has always been the focus problem of the power planning department and the power grid enterprises. In view of the complex nonlinear and nonstationary characteristics of the power grid investment demand sequence, a novel hybrid EMD-GASVM-RBFNN forecasting model based on empirical mode decomposition (EMD) method, support vector machines optimized by genetic algorithm (GA-SVM) model, and radial basis function neural network (RBFNN) model is proposed. Firstly, the EMD method is used to decompose the original power grid investment data sequence into a series of IMF components and a residual component which have stronger regularity compared with the original data. Then, according to the different characteristics of each subsequence, the GA-SVM and RBFNN model will be used to forecast different subsequences, respectively. Next, the prediction results of different subsequences are aggregated to obtain the final prediction results of the power grid investment. Finally, this paper dynamically simulates China's power grid investment from 2018 to 2020 based on the EMD-GASVM-RBFNN hybrid forecasting model and Monte Carlo method.