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.
Highlights
Since the end of the 20th century and during the early 21st century, topics related to renewable energy have increasingly come to the fore
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
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
Summary
Since the end of the 20th century and during the early 21st century, topics related to renewable energy have increasingly come to the fore. It is necessary to develop complementary mechanisms to those that already exist in the energy. The need is to satisfy electricity demands (International Energy Agency, 2018), followed by the secondary need to replace fossil fuels that contribute to increased pollution levels and whose future existence is questionable (Ritchie & Roser, 2017). Besides the known uses of energy, a new use has arisen owing to technological leaps in electric-powered transportation. Accurate predictions of the amount of renewable energy that can be produced are necessary since these installations are becoming integrated into the existing power generation and distribution infrastructure. The results of these predictions will represent the basis for future operational and strategic decisions with local and regional impact
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
