TITLE:
Wind Energy Prediction Using Machine Learning
AUTHORS:
Adrian-Nicolae Buturache, Stelian Stancu
KEYWORDS:
Artificial Neural Networks, Support Vector Machine, Regression Tree, Random Forest, Hyperparameter Tuning, Renewable Energy, Data-Driven Decision
JOURNAL NAME:
Low Carbon Economy,
Vol.12 No.1,
January
27,
2021
ABSTRACT: Wind energy prediction represents an important and active field in the
renewable energy sector. Since renewable energy sources are integrated into
existing grids and combined with traditional sources, knowing the amount of
energy that will be produced is key in minimizing the operational cost of the
wind farm and safe operation of the power grid. In this context, we propose a
comparative and comprehensive study of artificial neural networks, support
vector regression, random trees, and random forest, and present the pros and
cons of implementing the aforementioned techniques. A step-by-step approach
based on the CRISP-DM data mining framework reveals the thought process
end-to-end, including feature engineering, metrics selection, model selection,
or hyperparameter tuning. Using the selected
metrics for model evaluation, we provide a summary highlighting the optimal
results and the trade-off between performance and the resources expended to
achieve these results. This research is also intended to provide guidance for wind
energy professionals, filling the gap between purely academic research and
real-world business use cases, providing the exact architectures and
selected hyperparameters.