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

Qian Zhang, Kin Keung Lai, Dongxiao Niu, Qiang Wang and Xuebin Zhang
Additional contact information
Qian Zhang: School of Economics and Management, North China Electric Power University, Baoding 071003, China
Kin Keung Lai: Department of Management Science, City University of Hong Kong, Kowloon, Hong Kong
Dongxiao Niu: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Qiang Wang: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Xuebin Zhang: School of Economics and Management, North China Electric Power University, Baoding 071003, China

Energies, 2012, vol. 5, issue 9, 1-18

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
https://www.mdpi.com/1996-1073/5/9/3329/pdf (application/pdf)
https://www.mdpi.com/1996-1073/5/9/3329/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:5:y:2012:i:9:p:3329-3346:d:19867

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().