Abstract
This work proposes hybrid models combining time-series models (using linear functions) and artificial intelligence (using a nonlinear function) that can be used to provide monthly mean wind speed predictions for the Brazilian northeast region. These might be useful for wind power generation; for example, they could acquire important information on how the local wind potential can be usable for a possible wind power plant through understanding future wind speed values. To create the proposed hybrid models, it was necessary to set the wind speed variable as a dependent variable of exogenous variables (i.e., pressure, temperature, and precipitation). Thus, it was possible to consider the meteorological characteristics of the study regions. It is possible to verify the hybrid models’ efficiency in providing perfect adjustments to the observed data. This statement is based on the low values found in the error statistical analysis, i.e., an error of approximately 5.0% and a Nash–Sutcliffe coefficient near to 0.96. These results were certainly important in predicting the wind speed time-series, which was similar to the observed wind speed time-series profile. Great similarities of maximums and minimums between the series were evident and showed the capacity of the models to represent the seasonality characteristics.
Highlights
The development of renewable energy research is growing all over the world
This paper proposes hybrid models which combine time-series models with artificial intelligence, with the main objective of providing monthly mean wind speed predictions for the northeast region of Brazil
Rainfall being set as an exogenous variable in the making of the ARIMAX models was motivated by the influence that it exerts on the intensity of wind speed in the northeastern region of Brazil, as commented in [23,24,25]
Summary
The development of renewable energy research is growing all over the world. This can be justified by the occurrence of climate change, which many scientists claim is a result of increased anthropogenic gases emission (i.e., CO2 , NOx , SOx ). Climate change causes negative effects such as increased greenhouse effect, acid rain, and the degradation of the ozone layer [1]. Renewable energy sources can give future generations the opportunity to live in a healthier world and are fundamental to humanity. It is, necessary to invest more on clean and renewable energy research in order to reach sustainable development [2]
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