Next Article in Journal
Combustion and Heat Release Characteristics of Biogas under Hydrogen- and Oxygen-Enriched Condition
Next Article in Special Issue
Towards Cost and Comfort Based Hybrid Optimization for Residential Load Scheduling in a Smart Grid
Previous Article in Journal
Theoretical Study of the Effects of Spark Timing on the Performance and Emissions of a Light-Duty Spark Ignited Engine Running under Either Gasoline or Ethanol or Butanol Fuel Operating Modes
Previous Article in Special Issue
An Innovative Hybrid Model Based on Data Pre-Processing and Modified Optimization Algorithm and Its Application in Wind Speed Forecasting
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Energies 2017, 10(8), 1196; https://doi.org/10.3390/en10081196

Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Weight Determination Method

1
School of Economics and Management, North China Electric Power University, Beijing 102206, China
2
School of Education Intelligent Technology, Jiangsu Normal University, Xuzhou 221116, China
3
Department of Information Management, Oriental Institute of Technology, New Taipei 220, Taiwan
*
Author to whom correspondence should be addressed.
Received: 26 May 2017 / Revised: 10 August 2017 / Accepted: 11 August 2017 / Published: 13 August 2017
Full-Text   |   PDF [2322 KB, uploaded 13 August 2017]   |  

Abstract

Stable and accurate forecasting of icing thickness is of great significance for the safe operation of the power grid. In order to improve the robustness and accuracy of such forecasting, this paper proposes an innovative combination forecasting model using a modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) based on the variance-covariance (VC) weight determination method. Firstly, the initial weights and thresholds of BPNN are optimized by mind evolutionary computation (MEC) to prevent the BPNN from falling into local optima and speed up its convergence. Secondly, a bat algorithm (BA) is utilized to optimize the key parameters of SVM. Thirdly, the kernel function is introduced into an extreme learning machine (ELM) to improve the regression prediction accuracy of the model. Lastly, after adopting the above three modified models to predict, the variance-covariance weight determination method is applied to combine the forecasting results. Through performance verification of the model by real-world examples, the results show that the forecasting accuracy of the three individual modified models proposed in this paper has been improved, but the stability is poor, whereas the combination forecasting method proposed in this paper is not only accurate, but also stable. As a result, it can provide technical reference for the safety management of power grid. View Full-Text
Keywords: icing forecasting; back propagation neural network; mind evolutionary computation; bat algorithm; support vector machine; extreme learning machine with kernel; variance-covariance icing forecasting; back propagation neural network; mind evolutionary computation; bat algorithm; support vector machine; extreme learning machine with kernel; variance-covariance
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.; Liang, Y.; Wang, H.; Wang, M.; Hong, W.-C. Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Weight Determination Method. Energies 2017, 10, 1196.

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]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top