Esta é uma versão desatualizada publicada em 2020-12-31. Leia a versão mais recente.

Wind Speed Seasonality in a Brazilian Amazon-Savanna Region from the Global Land Data Assimilation System

Autores

DOI:

https://doi.org/10.5902/2179460X41092

Palavras-chave:

Energy planning, environmental modeling, remote sensing

Resumo

The objective of this study was to develop a methodology for the use of remote sensing data for the planning of wind energy projects in Maranhão. Monthly wind speed and precipitation data from 2000 to 2016 were used. Initially, wind velocity data were processed using the principal component analysis (PCA) technique. Next, the grouping technique known as k-means was used. Finally, a linear regression analysis was performed with the objective of identifying the parameters to be used in the validation of the data estimated by the Global Land Data Assimilation System (GLDAS) base against the data measured by the meteorological stations. Four homogeneous zones were identified; the zone with the highest values of monthly average wind speeds is in the northern region of the state on the coast. The period of greatest intensity of the winds was identified to be in the months of October and November. The lowest values of precipitation were observed during these months. The analyses carried out by this study show a favorable scenario for the production of wind energy in the state of Maranhão.

Downloads

Não há dados estatísticos.

Referências

EPE, E d P E, BalançoEnergético Nacional 2016 - Ano base 2015, Brasília: Ministério de Minas e Energia, 2016.

FILHO, J D C S RIBEIRO, A, COSTA M H, COHEN J C P, ROCHA, E J P. Variaçãosazonal do balanço de radiaçãoemumafloresta tropical no nordeste da Amazônia, RevistaBrasileira de Meteorologia, 2006; 21, 318-330.

FEITOSA F E C S, BELO R C R, SANTOS, J R N, ARAUJO M L S, SANTOS, J. S.; SILVA, F. B. Influência das Mudanças de Tempo e VariabilidadeClimáticasobre a Produção de EnergiaFotovoltaica no Estado do Maranhão (Climate Risk for Photovoltaic Energy Production in the state of Maranhão). REVISTA BRASILEIRA DE GEOGRAFIA FÍSICA, v. 10, p. 1959-1973, 2018.

J JANGID, APURBA KB, MANOJ J, VISHALS, SINGH TP, PRADHAN BK, SANDIPAN D. Potential zones identification for harvesting wind energy resources in desert region of India – A multi criteria evaluation approach using remote sensing and GIS. Renewable and Sustainable Energy Reviews. 2016;65, 1-10.

PREM KC, SIRAJ A, VILASW. Comparative analysis of Weibull parameters for wind data measured from met-mast and remote sensing techniques. Renewable Energy. 2018; 115, 1153-1165.

MATHER P M, KOCH, MAGALY. Preprocessing of Remotely‐Sensed Data, Computer Processing of Remotely-Sensed Images: An Introduction, Fourth Edition, 2010, 87-124.

NORMAN WILDMANN, NIKOLA VASILJEVIC AND THOMAS GERZ, Wind turbine wake measurements with automatically adjusting scanning trajectories in a multi-Doppler lidar setup, Atmospheric Measurement Techniques, 2018;6, 3801-3814.

R. KRISHNAMURTHY, J. REUDER, B. SVARDAL, H.J.S. FERNANDO AND J.B. JAKOBSEN. Offshore Wind Turbine Wake characteristics using Scanning Doppler Lidar, Energy Procedia. 2017; 137, 428-442.

SILVA F B, SANTOS J R N, FEITOSA F E C S, SILVA I D C, ARAÚJO M L S, GUTERRES C E, SANTOS J S RIBEIRO, C V, BEZERRA D S, NERES R L. Evidências de MudançasClimáticasnaRegião de Transição Amazônia-Cerrado no Estado do Maranhão. RevistaBrasileira de Meteorologia (Impresso), 2016; 31, 330-336.

STEFAN E, Wind Data Sources, Wind Energy Meteorology, 2018; 10.1007/978-3-319-72859-9_7, 183-230.

STEFAN EMEIS, NORBERT KALTHOFF, BIANCA ADLER, ERIC PARDYJAK, ALEXANDRE. Paci and Wolfgang Junkermann, High-Resolution Observations of Transport and Exchange Processes in Mountainous Terrain, Atmosphere, 2018; 9, 457.

Publicado

2020-12-31

Versões

Como Citar

Rodrigues, L. H. de S., Freitas, M. A. A., Pereira, L. V. S., Dias, B. C. C., Silvino, V. M., Passinho, J. N., & Silva, F. B. (2020). Wind Speed Seasonality in a Brazilian Amazon-Savanna Region from the Global Land Data Assimilation System. Ciência E Natura, 42, e12. https://doi.org/10.5902/2179460X41092

Artigos mais lidos pelo mesmo(s) autor(es)

Artigos Semelhantes

Você também pode iniciar uma pesquisa avançada por similaridade para este artigo.