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Prediction of the Standard Deviation of Wind Speed Turbulence
KTH. Harbin Inst Technol, Sch Energy Sci & Engn, Harbin, Heilongjiang, Peoples R China.;KTH Royal Inst Technol, Dept Elect Power & Energy Syst, Stockholm, Sweden..
Harbin Inst Technol, Sch Energy Sci & Engn, Harbin, Heilongjiang, Peoples R China..
Harbin Inst Technol, Sch Energy Sci & Engn, Harbin, Heilongjiang, Peoples R China..
Harbin Inst Technol, Sch Energy Sci & Engn, Harbin, Heilongjiang, Peoples R China..
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2018 (English)In: JOURNAL OF ENVIRONMENTAL INFORMATICS, ISSN 1726-2135, Vol. 32, no 1, p. 1-13Article in journal (Refereed) Published
Abstract [en]

Turbulent wind speed is a vital component of wind speed, corresponding to the small-scale uncertainty of wind speed. The wind power output and aerodynamic loads are significantly influenced by turbulent wind speed. The present paper concentrates on the study of turbulent wind speed prediction, which is hardly addressed in previous studies and is a novel approach to understand turbulent wind speed in advance. In the present study, turbulent wind speed is measured by turbulence standard deviation. Although turbulent wind speed itself is unpredictable, the correlation analysis proves that the turbulence standard deviation is predictable within a proper time horizon. As a result, the turbulent wind speed can be understood ahead of time by predicting turbulence standard deviation. The 10-min ahead, 30-min ahead and 60-min ahead predictions of turbulence standard deviation are provided by Support Vector Regression and Kernel Ridge Regression. Furthermore, the average wind speed is fused into the forecasting model to improve the prediction accuracy of turbulence standard deviation.

Place, publisher, year, edition, pages
INT SOC ENVIRON INFORM SCI , 2018. Vol. 32, no 1, p. 1-13
Keywords [en]
Wind speed, Turbulence, Uncertainty, Standard deviation, Predictability, Correlation analysis
National Category
Fluid Mechanics and Acoustics
Identifiers
URN: urn:nbn:se:kth:diva-236019DOI: 10.3808/jei.201800389ISI: 000446093900001Scopus ID: 2-s2.0-85062461358OAI: oai:DiVA.org:kth-236019DiVA, id: diva2:1255692
Note

QC 20181012

Available from: 2018-10-15 Created: 2018-10-15 Last updated: 2020-03-09Bibliographically approved

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