Next Article in Journal
Multi-Plane Ultrafast Compound 3D Strain Imaging: Experimental Validation in a Carotid Bifurcation Phantom
Previous Article in Journal
Histological and Bone Morphometric Evaluation of Osseointegration Aspects by Alkali Hydrothermally-Treated Implants
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(4), 636; https://doi.org/10.3390/app8040636

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

1,2,* , 1,2
and
1,2
1
School of Economics and Management, North China Electric Power University, Beijing 102206, China
2
Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China
*
Author to whom correspondence should be addressed.
Received: 20 March 2018 / Revised: 7 April 2018 / Accepted: 16 April 2018 / Published: 19 April 2018
(This article belongs to the Section Energy)
Full-Text   |   PDF [6961 KB, uploaded 3 May 2018]   |  

Abstract

In recent years, the construction of China’s power grid has experienced rapid development, and its scale has leaped into the first place in the world. Accurate and effective prediction of power grid investment can not only help pool funds and rationally arrange investment in power grid construction, but also reduce capital costs and economic risks, which plays a crucial role in promoting power grid investment planning and construction process. In order to forecast the power grid investment of China accurately, firstly on the basis of analyzing the influencing factors of power grid investment, the influencing factors system for China’s power grid investment forecasting is constructed in this article. The method of grey relational analysis is used for screening the main influencing factors as the prediction model input. Then, a novel power grid investment prediction model based on DE-GWO-SVM (support vector machine optimized by differential evolution and grey wolf optimization) algorithm is proposed. Next, two cases are taken for empirical analysis to prove that the DE-GWO-SVM model has strong generalization capacity and has achieved a good prediction effect for power grid investment forecasting in China. Finally, the DE-GWO-SVM model is adopted to forecast power grid investment in China from 2018 to 2022. View Full-Text
Keywords: power grid investment forecasting; influencing factors screening; support vector machine; differential evolution; grey wolf optimization power grid investment forecasting; influencing factors screening; support vector machine; differential evolution; grey wolf optimization
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Dai, S.; Niu, D.; Han, Y. Forecasting of Power Grid Investment in China Based on Support Vector Machine Optimized by Differential Evolution Algorithm and Grey Wolf Optimization Algorithm. Appl. Sci. 2018, 8, 636.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top