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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
School of Economics and Management, North China Electric Power University, Baoding 071003, China
2
Department of Management Science, City University of Hong Kong, Kowloon, Hong Kong
3
School of Economics and Management, North China Electric Power University, Beijing 102206, China
*
Author to whom correspondence should be addressed.
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)
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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. View Full-Text
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 (CC BY 3.0).

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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.

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