A Review of Short-Term Wind Power Forecasting Based on Artificial Intelligence Methods

With the advancements in new power systems and renewable energy technologies, such as wind power, its role in the energy field has become increasingly significant. Wind power prediction stands as a fundamental task in grid scheduling and energy distribution. Presently, wind power prediction primarily relies on historical data regression. However, the inherent volatility and intermittency of wind power generation contribute to grid connection instability. In response to the growing demand for integrating large-scale wind power into the grid and enhancing short-term wind power prediction accuracy, various artificial intelligence-based techniques have been continuously proposed. These techniques have demonstrated promising performance across different applications. This paper reviews the current state-of-the-art wind power prediction methods, highlighting their contributions while delineating their respective advantages and limitations. Moreover, it outlines potential avenues for future advancements in this domain. Finally, an outlook for future research is given with a view that the research done in this paper can be useful for other researchers.