  The transition from conventional methods of energy production to renewable
energy production necessitates better prediction models of the upcoming supply
of renewable energy. In wind power production, error in forecasting production
is impossible to negate owing to the intermittence of wind. For successful
power grid integration, it is crucial to understand the uncertainties that
arise in predicting wind power production and use this information to build an
accurate and reliable forecast. This can be achieved by observing the
fluctuations in wind power production with changes in different parameters such
as wind speed, temperature, and wind direction, and deriving functional
dependencies for the same. Using optimized machine learning algorithms, it is
possible to find obscured patterns in the observations and obtain meaningful
data, which can then be used to accurately predict wind power requirements .
Utilizing the required data provided by the Gamesa's wind farm at Bableshwar,
the paper explores the use of both parametric and the non-parametric models for
calculating wind power prediction using power curves. The obtained results are
subject to comparison to better understand the accuracy of the utilized models
and to determine the most suitable model for predicting wind power production
based on the given data set.
