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.
Highlights
Power grid is a basic carrier of electricity collection, transmission, and allocation; it is an important guarantee for the healthy development of the economy and society
In the light of the defects and advantages of existing power grid investment forecasting models, this paper proposes a novel empirical mode decomposition (EMD)-GASVM-radial basis function neural network (RBFNN) hybrid model to forecast China’s power grid investment based on the historical data from 1990 to 2017
Based on the calculation results of the mean absolute percentage error (MAPE) and root mean square error (RMSE), we can conclude that the EMD hybrid forecasting model has a better prediction performance than the genetic algorithm (GA)-support vector machine (SVM) model and BP neural network
Summary
Power grid is a basic carrier of electricity collection, transmission, and allocation; it is an important guarantee for the healthy development of the economy and society. The support vector machine can effectively process the complex nonlinear data and brilliant parameters optimization ability of the intelligent algorithm; the DE-GWO-SVM model has an outstanding prediction performance for China’s power grid investment. (1) The empirical mode decomposition (EMD) is used to decompose the original data of China’s power grid investment which has complex nonlinear, nonstationary characteristics from 1990 to 2017 into several intrinsic mode function (IMF) components and a residual component which represent the random characteristics, periodic characteristics, and trend characteristics of the original power grid investment data, respectively These subsequences decomposed from the original power grid investment data by the EMD method have simpler frequency feature and stronger correlations [5] which are easier for building the prediction model than the original data series and which improve the prediction accuracy prominently. The main structure of this article is arranged as follows: Section 2 introduces the methodology; Section 3 carries out empirical analysis to verify the validity of the proposed model for the power grid investment prediction in China; Section 4 uses the Monte Carlo dynamic simulation method to simulate the power grid investment in China from 2018 to 2020; Section 5 summarizes the whole paper
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