Determination of Acceptable Sampling Rates for Recording Wind-Speed Data

1979 ◽  
Vol 22 (5) ◽  
pp. 1175-1177 ◽  
Author(s):  
R. C. Werner ◽  
L. H. Soderholm ◽  
R. H. Shaw
Keyword(s):  
Wind Speed ◽  
Wind Speed Data

Regionalization of wind-speed data to analyse tree-line wind conditions in the eastern Andes of southern Ecuador

Erdkunde ◽  
2015 ◽  
Vol 69 (1) ◽  
pp. 3-19 ◽  
Author(s):  
Julia Wagemann ◽  
Boris Thies ◽  
Rütger Rollenbeck ◽  
Thorsten Peters ◽  
Jörg Bendix
Keyword(s):  
Wind Speed ◽  
Tree Line ◽  
Wind Speed Data ◽  
Southern Ecuador

scholarly journals Evaluating the potential of offshore wind energy in the Gulf of Oman using the MENA-CORDEX wind speed data simulations

2021 ◽  
Vol 15 (1) ◽  
pp. 613-626
Author(s):  
Shahab S. Band ◽  
Sayed M. Bateni ◽  
Mansour Almazroui ◽  
Shahin Sajjadi ◽  
Kwok-wing Chau ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Offshore Wind ◽  
Offshore Wind Energy ◽  
Gulf Of Oman ◽  
Wind Speed Data

Verifying Meteorological Station Wind Speed Data for Long-Term Resource Studies: The MIA Luqa Wind Databases at Malta

2013 ◽  
Vol 37 (6) ◽  
pp. 605-616 ◽  
Author(s):  
L. Mayor Salgado ◽  
R.N. Farrugia ◽  
C. Galdies ◽  
T. Sant
Keyword(s):  
Wind Speed ◽  
Meteorological Station ◽  
Wind Speed Data

Stochastic generation of hourly mean wind speed data

2004 ◽  
Vol 29 (14) ◽  
pp. 2111-2131 ◽  
Author(s):  
Hafzullah Aksoy ◽  
Z Fuat Toprak ◽  
Ali Aytek ◽  
N Erdem Ünal
Keyword(s):  
Wind Speed ◽  
Stochastic Generation ◽  
Wind Speed Data ◽  
Mean Wind Speed

A statistical Time-Of-Use tariff based wind resource clustering approach using Self-Organizing Maps

2021 ◽  
pp. 0309524X2110287
Author(s):  
Chantelle Y Janse van Vuuren ◽  
Hendrik J Vermeulen ◽  
Matthew Groch
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Variable Component ◽  
Self Organizing Maps ◽  
Wind Speed Data ◽  
Main Driver ◽  
Clustering Approach ◽  
Statistical Time ◽  
Data Reduction Technique ◽  
Self Organizing

The optimized siting of grid-scale renewable generation is a viable technique to minimize the variable component of the electricity generation portfolio. This process, however, requires simulated meteorological datasets, and consequently, significant computational power to perform detailed studies. This is particularly true for countries with large geographic areas. Clustering is a viable data reduction technique that can be utilized to reduce the computational burden. This work proposes the use of Self-Organizing Maps to partition high-dimensional wind speed data using statistical features derived from Time-Of-Use tariff periods. This approach is undertaken with the view towards the optimization of wind farm siting for grid-support objectives where tariff incentivization is the main driver. The proposed approach is compared with clusters derived using Self-Organizing Maps with the temporal wind speed data for the input feature set. The results show increased cluster granularity, superior validation results and decreased execution time when compared with the temporal clustering approach.

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