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Open AccessArticle

A Least Squares Support Vector Machine Optimized by Cloud-Based Evolutionary Algorithm for Wind Power Generation Prediction

Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071003, China
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Author to whom correspondence should be addressed.
Academic Editor: José C. Riquelme
Energies 2016, 9(8), 585; https://doi.org/10.3390/en9080585
Received: 14 May 2016 / Revised: 7 July 2016 / Accepted: 20 July 2016 / Published: 28 July 2016
(This article belongs to the Special Issue Energy Time Series Forecasting)
Accurate wind power generation prediction, which has positive implications for making full use of wind energy, seems still a critical issue and a huge challenge. In this paper, a novel hybrid approach has been proposed for wind power generation forecasting in the light of Cloud-Based Evolutionary Algorithm (CBEA) and Least Squares Support Vector Machine (LSSVM). In order to improve the forecasting precision, a two-way comparison approach is conducted to preprocess the original wind power generation data. The pertinent parameters of LSSVM are optimized by using CBEA to verify the learning and generalization abilities of the LSSVM model. The experimental results indicate that the forecasting performance of the proposed model is better than the single LSSVM model and all of the other models for comparison. Moreover, the paired-sample t-test is employed to cast light on the applicability of the developed model. View Full-Text
Keywords: two-way comparison; least squares support vector machine; cloud-based evolutionary algorithm; paired-sample t-test; wind power generation prediction two-way comparison; least squares support vector machine; cloud-based evolutionary algorithm; paired-sample t-test; wind power generation prediction
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MDPI and ACS Style

Wu, Q.; Peng, C. A Least Squares Support Vector Machine Optimized by Cloud-Based Evolutionary Algorithm for Wind Power Generation Prediction. Energies 2016, 9, 585.

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