Renewable energy is the current trend of energy sourcing.
Numerous scientists, inventors, and engineers are working hard to harness renewable energy.
The application of renewable energy is very wide; it can be as small as lighting an LED bulb or as large as generating the electricity of a town or even a country.
Wind energy plays an important role in the context of electricity generation.
Wind energy is highly dependent on the wind speed at a wind site.
Wind prediction is necessary for a wind energy assessment of a potential wind farm.
In this study, the wind energy assessment is based on wind prediction using the Mycielski algorithm and K-means clustering in Kudat, Malaysia.
The predicted results are analysed using Weibull analysis to obtain the most probable wind speed.
From the result soft his study, K-means clustering is more accurate in prediction when compared with the Mycielski algorithm.
The most probable wind in Kudat is sufficient to operate the wind turbines