Next Article in Journal
An Intelligent Multiagent System for Autonomous Microgrid Operation
Next Article in Special Issue
An Improved Quantum-Behaved Particle Swarm Optimization Method for Economic Dispatch Problems with Multiple Fuel Options and Valve-Points Effects
Previous Article in Journal
Response Surface Methodology: An Emphatic Tool for Optimized Biodiesel Production Using Rice Bran and Sunflower Oils
Energies 2012, 5(9), 3329-3346; doi:10.3390/en5093329

A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power

1,* , 2,3
Received: 20 April 2012 / Revised: 15 August 2012 / Accepted: 21 August 2012 / Published: 5 September 2012
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
Download PDF [334 KB, uploaded 17 March 2015]


Many models have been developed to forecast wind farm power output. It is generally difficult to determine whether the performance of one model is consistently better than that of another model under all circumstances. Motivated by this finding, we aimed to integrate groups of models into an aggregated model using fuzzy theory to obtain further performance improvements. First, three groups of least squares support vector machine (LS-SVM) forecasting models were developed: univariate LS-SVM models, hybrid models using auto-regressive moving average (ARIMA) and LS-SVM and multivariate LS-SVM models. Each group of models is selected by a decorrelation maximisation method, and the remaining models can be regarded as experts in forecasting. Next, fuzzy aggregation and a defuzzification procedure are used to combine all of these forecasting results into the final forecast. For sample randomization, we statistically compare models. Results show that this group-forecasting model performs well in terms of accuracy and consistency.
Keywords: wind power forecasting; LS-SVM; ARIMA; fuzzy group wind power forecasting; LS-SVM; ARIMA; fuzzy group
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.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
MDPI and ACS Style

Zhang, Q.; Lai, K.K.; Niu, D.; Wang, Q.; Zhang, X. A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power. Energies 2012, 5, 3329-3346.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here


Cited By

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