Current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: A review

• Published in: Engineering reports (Hoboken, N.J.) N.J.), 2020-06, Vol.2 (6), p.n/a
• Main Authors: Santhosh, Madasthu, Venkaiah, Chintham, Vinod Kumar, D. M.
• Format: Article
• Language: English
• Subjects: artificial intelligence; decomposition‐based models; deep learning; hybrid prediction; numerical weather prediction; wind speed and wind power forecasting
• ISSN: 2577-8196; 2577-8196
• DOI: 10.1002/eng2.12178

Wind power is playing a pivotal part in global energy growth as it is clean and pollution‐free. To maximize profits, economic scheduling, dispatching, and planning the unit commitment, there is a great demand for wind forecasting techniques. This drives the researchers and electric utility planners in the direction of more advanced approaches to forecast over broader time horizons. Key prediction techniques use physical, statistical approaches, artificial intelligence techniques, and hybrid methods. An extensive review of the current forecasting techniques, as well as their performance evaluation, is here presented. The techniques used for improving the prediction accuracy, methods to overcome major forecasting problems, evolving trends, and further advanced applications in future research are explored. One of the most evolving renewable energy systems, wind energy is playing a pivotal role in global energy growth as it is clean and pollution‐free. In order to maximize profits, economic scheduling, dispatching and planning the unit commitment there is a great demand for wind speed and wind power forecasting methods. An extensive review of current forecasting techniques, as well as their performance evaluation, is presented. The techniques for enhancing accuracy with major forecasting problems, evolving trends and further advanced applications in future research are explored.