On the distributional parameters used in assessment of the suitability of wind speed probability density functions

By A. Celik. 2004

97 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/78cde713ab34c5a021cad18bb90d14086ec9c2fa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Jing Shi
2. Ergin Erdem
3. Gong Li
4. Junyi Zhou
5. E. Akpinar

In Environmental Engineering

1. Jing Shi
2. Ergin Erdem
3. Gong Li
4. Junyi Zhou
5. E. Akpinar

Citations in the corpus

• Comprehensive evaluation of wind speed distribution models: A case study for North Dakota sites (2010, methodology): “Celik [7] uses both 2-parameter Rayleigh and Weibull distributions to fit an hourly wind speed dataset of one year, and find that Weibull distribution results more accurate wind energy prediction.”
• A Statistical Investigation of Wind Energy Potential (2006, methodology): “…Weibull probability density function can be expressed as follows: PW = 1 2 ρc3 ( 1 + 3 k ) (9) Setting k equal to 2, the power density for the Rayleigh density function is found to be (Celik, 2004a), PR = 3 π ρv3m (10) The monthly wind characteristics for the whole stations are shown in Tables 2–5.”