Forecasting of Power Grid Investment in China Based on Support Vector Machine Optimized by Differential Evolution Algorithm and Grey Wolf Optimization Algorithm

[...]there are relatively few research results on power grid investment prediction, and the prediction models used by the researchers are relatively traditional, such as time series model, grey model, error correction model, and so on. [...]although the grey wolf optimization algorithm has advantage...

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Bibliographic Details
Published inApplied sciences Vol. 8; no. 4; p. 636
Main Authors Dai, Shuyu, Niu, Dongxiao, Han, Yaru
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2018
Subjects
Algorithms
Artificial intelligence
Cointegration analysis
differential evolution
Economic models
Error correction
Evolutionary algorithms
Evolutionary computation
Forecasting
Genetic algorithms
grey wolf optimization
influencing factors screening
Investment
Mathematical models
Neural networks
Optimization
power grid investment forecasting
Prediction models
support vector machine
Support vector machines
Beijing China
United States > US
China
India
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Summary:[...]there are relatively few research results on power grid investment prediction, and the prediction models used by the researchers are relatively traditional, such as time series model, grey model, error correction model, and so on. [...]although the grey wolf optimization algorithm has advantages in global search and convergence, with the increase of iteration times, it is also unavoidable to fall into the local optimum. [...]the optimized parameter values are assigned to SVM, and the forecasting model is established to predict the power grid investment. [...]the power grid investment prediction influencing factors system and the DE-GWO-SVM prediction model proposed in this article have achieved a good prediction effect for the power grid investment forecasting in China, which provides a new approach to research on power grid investment forecasting.
ISSN:2076-3417
2076-3417
DOI:10.3390/app8040636