  Electricity generation from burning fossil fuels is one of the major
contributors to global warming. Renewable energy sources are a viable
alternative to produce electrical energy and to reduce the emission from the
power industry. These energy sources are the building blocks of green energy,
which all have different characteristics. Their availabilities are also
diverse, depending on geographical locations and other parameters. Low
implementation cost and distributed availability all over the world uplifts
their popularity exponentially. Therefore, it has unlocked opportunities for
consumers to produce electricity locally and use it on-site, which reduces
dependency on centralized utility companies. The research considers two main
objectives: the prediction of wind speed that simplifies wind farm planning and
feasibility study. Secondly, the need to understand the dependency structure of
the wind speeds of multiple distant locations. To address the first objective,
twelve artificial intelligence algorithms were used for wind speed prediction
from collected meteorological parameters. The model performances were compared
to determine the wind speed prediction accuracy. The results show a deep
learning approach, long short-term memory (LSTM) outperforms other models with
the highest accuracy of 97.8%. For dependency, a multivariate cumulative
distribution function, Copula, was used to find the joint distribution of two
or more distant location wind speeds, followed by a case study. We found that
the appropriate copula family and the parameters vary based on the distance in
between. For the case study, Joe-Frank (BB8) copula shows an efficient joint
distribution fit for a wind speed pair with a standard error of 0.0094.
Finally, some insights about the uncertainty aspects of wind speed dependency
were addressed.
