Next Article in Journal
Modular Multilevel Converters: Control and Applications
Next Article in Special Issue
Applications of the Chaotic Quantum Genetic Algorithm with Support Vector Regression in Load Forecasting
Previous Article in Journal
Approach to Hybrid Energy Storage Systems Dimensioning for Urban Electric Buses Regarding Efficiency and Battery Aging
Previous Article in Special Issue
Towards Cost and Comfort Based Hybrid Optimization for Residential Load Scheduling in a Smart Grid
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Energies 2017, 10(11), 1713; doi:10.3390/en10111713

Applications of Hybrid EMD with PSO and GA for an SVR-Based Load Forecasting Model

1
College of Mathematics & Information Science, Ping Ding Shan University, Pingdingshan 467000, China
2
School of Education Intelligent Technology, Jiangsu Normal University, 101 Shanghai Rd., Tongshan District, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Received: 30 September 2017 / Revised: 19 October 2017 / Accepted: 21 October 2017 / Published: 26 October 2017
View Full-Text   |   Download PDF [5121 KB, uploaded 26 October 2017]   |  

Abstract

Providing accurate load forecasting plays an important role for effective management operations of a power utility. When considering the superiority of support vector regression (SVR) in terms of non-linear optimization, this paper proposes a novel SVR-based load forecasting model, namely EMD-PSO-GA-SVR, by hybridizing the empirical mode decomposition (EMD) with two evolutionary algorithms, i.e., particle swarm optimization (PSO) and the genetic algorithm (GA). The EMD approach is applied to decompose the load data pattern into sequent elements, with higher and lower frequencies. The PSO, with global optimizing ability, is employed to determine the three parameters of a SVR model with higher frequencies. On the contrary, for lower frequencies, the GA, which is based on evolutionary rules of selection and crossover, is used to select suitable values of the three parameters. Finally, the load data collected from the New York Independent System Operator (NYISO) in the United States of America (USA) and the New South Wales (NSW) in the Australian electricity market are used to construct the proposed model and to compare the performances among different competitive forecasting models. The experimental results demonstrate the superiority of the proposed model that it can provide more accurate forecasting results and the interpretability than others. View Full-Text
Keywords: support vector regression; empirical mode decomposition (EMD); particle swarm optimization (PSO); genetic algorithm (GA); load forecasting support vector regression; empirical mode decomposition (EMD); particle swarm optimization (PSO); genetic algorithm (GA); load forecasting
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).

Share & Cite This Article

MDPI and ACS Style

Fan, G.-F.; Peng, L.-L.; Zhao, X.; Hong, W.-C. Applications of Hybrid EMD with PSO and GA for an SVR-Based Load Forecasting Model. Energies 2017, 10, 1713.

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