İmir, Mehmet

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https://hdl.handle.net/20.500.14411/8161
Name Variants
İmir,M.
Imir,M.
Mehmet, Imir
M., Imir
I., Mehmet
İ.,Mehmet
Imir, Mehmet
Mehmet, İmir
I.,Mehmet
İmir, Mehmet
M.,Imir
M.,İmir
Job Title
Araştırma Görevlisi
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Energy Systems Engineering
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Former Staff
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NO POVERTY1
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CLEAN WATER AND SANITATION6
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AFFORDABLE AND CLEAN ENERGY7
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DECENT WORK AND ECONOMIC GROWTH8
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Scholarly Output

2

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WoS Citation Count

170

Scopus Citation Count

203

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WoS Citations per Publication

85.00

Scopus Citations per Publication

101.50

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JournalCount
4th International Conference on Nuclear and Renewable Energy Resources (NURER) -- OCT 26-29, 2014 -- Antalya, TURKEY1
Energy Conversion and Management1
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Has files: falseSubject: Wind power density

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    Citation - WoS: 68
    Citation - Scopus: 81
    Seasonal and Yearly Wind Speed Distribution and Wind Power Density Analysis Based on Weibull Distribution Function
    (Pergamon-elsevier Science Ltd, 2015) Bilir, Levent; Imir, Mehmet; Devrim, Yilser; Albostan, Ayhan
    Wind energy, which is among the most promising renewable energy resources, is used throughout the world as an alternative to fossil fuels. In the assessment of wind energy for a region, the use of two-parameter Weibull distribution is an important tool. In this study, wind speed data, collected for a one year period between June 2012 and June 2013, were evaluated. Wind speed data, collected for two different heights (20 m and 30 m) from a measurement station installed in Atihm University campus area (Ankara, Turkey), were recorded using a data logger as one minute average values. Yearly average hourly wind speed values for 20 m and 30 m heights were determined as 2.9859 m/s and 3.3216 m/s, respectively. Yearly and seasonal shape (k) and scale (c) parameter of Weibull distribution for wind speed were calculated for each height using five different methods. Additionally, since the hub height of many wind turbines is higher than these measurement heights, Weibull parameters were also calculated for 50 m height. Root mean square error values of Weibull distribution functions for each height, derived using five different methods, show that a satisfactory representation of wind data is achieved for all methods. Yearly and seasonal wind power density values of the region were calculated using the best Weibull parameters for each case. As a conclusion, the highest wind power density value was found to be in winter season while the lowest value was encountered in autumn season. Yearly wind power densities were calculated as 39.955 (W/m(2)), 51.282 (W/m(2)) and 72.615 (W/m(2)) for 20 m, 30 m and 50 m height, respectively. The prevailing wind direction was also determined as southeast for the region. It can be concluded that the wind power density value at the region is considerable and can be exploited using small scale wind turbines. Copyright (C) 2015, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.