Next Article in Journal
Investigation of a Co-Axial Dual-Mechanical Ports Flux-Switching Permanent Magnet Machine for Hybrid Electric Vehicles
Previous Article in Journal
Optimal Operation of Combined Heat and Power System Based on Forecasted Energy Prices in Real-Time Markets
Article Menu

Export Article

Open AccessArticle
Energies 2015, 8(12), 14346-14360; doi:10.3390/en81212428

Wind Power Grid Connected Capacity Prediction Using LSSVM Optimized by the Bat Algorithm

Department of Economics and Management, North China Electric Power University, Baoding 071003, China
*
Author to whom correspondence should be addressed.
Academic Editor: Frede Blaabjerg
Received: 21 November 2015 / Revised: 14 December 2015 / Accepted: 15 December 2015 / Published: 18 December 2015
View Full-Text   |   Download PDF [1878 KB, uploaded 18 December 2015]   |  

Abstract

Given the stochastic nature of wind, wind power grid-connected capacity prediction plays an essential role in coping with the challenge of balancing supply and demand. Accurate forecasting methods make enormous contribution to mapping wind power strategy, power dispatching and sustainable development of wind power industry. This study proposes a bat algorithm (BA)–least squares support vector machine (LSSVM) hybrid model to improve prediction performance. In order to select input of LSSVM effectively, Stationarity, Cointegration and Granger causality tests are conducted to examine the influence of installed capacity with different lags, and partial autocorrelation analysis is employed to investigate the inner relationship of grid-connected capacity. The parameters in LSSVM are optimized by BA to validate the learning ability and generalization of LSSVM. Multiple model sufficiency evaluation methods are utilized. The research results reveal that the accuracy improvement of the present approach can reach about 20% compared to other single or hybrid models. View Full-Text
Keywords: wind power grid connected capacity prediction; bat algorithm (BA); least squares support vector machine (LSSVM); Granger causality test wind power grid connected capacity prediction; bat algorithm (BA); least squares support vector machine (LSSVM); Granger causality test
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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Wu, Q.; Peng, C. Wind Power Grid Connected Capacity Prediction Using LSSVM Optimized by the Bat Algorithm. Energies 2015, 8, 14346-14360.

Show more citation formats Show less citations formats

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