Next Article in Journal
Motives for Corporate Social Responsibility in Chinese Food Companies
Next Article in Special Issue
The Difficulty of Climate Change Adaptation in Manufacturing Firms: Developing an Action-Theoretical Perspective on the Causality of Adaptive Inaction
Previous Article in Journal
A Study on Creative Climate in Project-Organized Groups (POGs) in China and Implications for Sustainable Pedagogy
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Sustainability 2018, 10(1), 118; https://doi.org/10.3390/su10010118

Cost Forecasting of Substation Projects Based on Cuckoo Search Algorithm and Support Vector Machines

School of Economics and Management, North China Electric Power University, Beijing 102206, China
*
Author to whom correspondence should be addressed.
Received: 4 December 2017 / Revised: 30 December 2017 / Accepted: 31 December 2017 / Published: 5 January 2018
(This article belongs to the Special Issue Circular Economy, Ethical Funds, and Engineering Projects)
View Full-Text   |   Download PDF [1341 KB, uploaded 5 January 2018]   |  

Abstract

Accurate prediction of substation project cost is helpful to improve the investment management and sustainability. It is also directly related to the economy of substation project. Ensemble Empirical Mode Decomposition (EEMD) can decompose variables with non-stationary sequence signals into significant regularity and periodicity, which is helpful in improving the accuracy of prediction model. Adding the Gauss perturbation to the traditional Cuckoo Search (CS) algorithm can improve the searching vigor and precision of CS algorithm. Thus, the parameters and kernel functions of Support Vector Machines (SVM) model are optimized. By comparing the prediction results with other models, this model has higher prediction accuracy. View Full-Text
Keywords: cost prediction of substation project; Ensemble Empirical Mode Decomposition; Cuckoo Search; Support Vector Machines cost prediction of substation project; Ensemble Empirical Mode Decomposition; Cuckoo Search; Support Vector Machines
Figures

Figure 1

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

Niu, D.; Zhao, W.; Li, S.; Chen, R. Cost Forecasting of Substation Projects Based on Cuckoo Search Algorithm and Support Vector Machines. Sustainability 2018, 10, 118.

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]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top