LAPSE:2019.0100
Published Article
LAPSE:2019.0100
A Least Squares Support Vector Machine Optimized by Cloud-Based Evolutionary Algorithm for Wind Power Generation Prediction
January 7, 2019
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.
Record ID
Keywords
cloud-based evolutionary algorithm, least squares support vector machine, paired-sample t-test, two-way comparison, wind power generation prediction
Subject
Suggested Citation
Wu Q, Peng C. A Least Squares Support Vector Machine Optimized by Cloud-Based Evolutionary Algorithm for Wind Power Generation Prediction. (2019). LAPSE:2019.0100
Author Affiliations
Journal Name
Energies
Volume
9
Issue
8
Article Number
E585
Year
2016
Publication Date
2016-07-28
Published Version
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en9080585, Publication Type: Journal Article
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Published Article
LAPSE:2019.0100
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External Link
doi:10.3390/en9080585
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Version History
[v1] (Original Submission)
Jan 7, 2019
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Jan 7, 2019
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https://psecommunity.org/LAPSE:2019.0100
Original Submitter
Calvin Tsay
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