Affiliation:
1. School of Automation, Central South University, Changsha 410083, China
2. Department of Electric Stations, Grids and Power, Supply Systems, South Ural State University, 76 Prospekt Lenina, 454080 Chelyabinsk, Russia
Abstract
Wind prediction has consistently been in the spotlight as a crucial element in achieving efficient wind power generation and reducing operational costs. In recent years, with the rapid advancement of artificial intelligence (AI) technology, its application in the field of wind prediction has made significant strides. Focusing on the process of AI-based wind prediction modeling, this paper provides a comprehensive summary and discussion of key techniques and models in data preprocessing, feature extraction, relationship learning, and parameter optimization. Building upon this, three major challenges are identified in AI-based wind prediction: the uncertainty of wind data, the incompleteness of feature extraction, and the complexity of relationship learning. In response to these challenges, targeted suggestions are proposed for future research directions, aiming to promote the effective application of AI technology in the field of wind prediction and address the crucial issues therein.
Funder
National Natural Science Foundation of China
National Research Foundation of Korea
Natural Science Foundation of Hunan Province
Natural Science Foundation of Changsha
Reference132 articles.
1. Sensing as the key to the safety and sustainability of new energy storage devices;Yi;Prot. Control Mod. Power Syst.,2023
2. Wind Power Prediction Based on Multi-class Autoregressive Moving Average Model with Logistic Function;Dong;J. Mod. Power Syst. Clean Energy,2022
3. Ultra-Short-Term Interval Prediction of Wind Power Based on Graph Neural Network and Improved Bootstrap Technique;Liao;J. Mod. Power Syst. Clean Energy,2023
4. Grouping-Based Optimal Design of Collector System Topology for a Large-Scale Offshore Wind Farm by Improved Simulated Annealing;Chen;Prot. Control Mod. Power Syst.,2024
5. Song, D., Shen, G., Huang, C., Huang, Q., Yang, J., Dong, M., Joo, Y.H., and Duić, N. (2024). Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems. J. Mar. Sci. Eng., 12.
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