Predicting wind speed is a complex task that involves analyzing various
meteorological factors such as temperature, humidity, atmospheric pressure, and
topography. There are different approaches
that can be used to predict wind speed,
and a hybrid optimization approach is one of them. In this paper, the hybrid
optimization approach combines a multiple linear regression approach with
an optimization technique to achieve better results. In the context of wind
speed prediction, this hybrid optimization approach can be used to improve the
accuracy of existing prediction models. Here, a Grey Wolf Optimizer based Wind
Speed Prediction (GWO-WSP) method is proposed. This approach is tested on the
2016, 2017, 2018, and 2019 Raw Data files from the Great Lakes Environmental
Research Laboratories and the National Oceanic and Atmospheric Administration’s
(GLERL-NOAA) Chicago Metadata Archive. The test results show that the
implementation is successful and the approach yields accurate and feasible
results. The computation time for execution of the algorithm is also superior
compared to the existing methods in literature.
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