Análise da complexidade das séries de velocidade do vento no Nordeste do Brasil

Abstract

Quantifying the complexity of non-stationary time series is of great importance for climatic data analysis. This approach presents a deeper insight into the mechanisms governing the processes involved in meteorological phenomena. This work aimed to study temporal and spatial variability of wind speed time series of 123 meteorological stations of the Northeast region of Brazil. The regularity of winds was quantified by applying the permutation entropy method, which incorporates temporal relationship between the values of the analyzed series, using a symbolic, natural representation, based on comparison of consecutive values of the series. The wind potential index (IPE) was defined considering that higher wind speed and lower entropy (higher predictability) are favorable conditions for the generation of wind energy. In order to spatially represent the results of this analysis, the Kernel Smoothing spatial interpolation technique was used, which is widely applied in various studies with climatic data. The results of the analysis of the wind speed series, for the period from 2008 to 2015, showed that the values of the wind potential index were higher in the States from Ceará to Pernambuco for the period from September to November. It was also found that the values of IPE decreased as the stations moved away from the sea. Another result was the decrease in entropy values with increase of embedding dimension d, reflecting the persistence of wind dynamics. The lower entropy was observed in Rio Grande do Norte, which also has the highest wind potential (highest mean wind speed), indicating that in this region the wind dynamics is more regular and more predictable, which is favorable for wind power generation. This behavior was also observed in the monthly and seasonal scale in the states of Rio Grande do Norte, Ceará and Bahia.