A Survey of Artificial Intelligence Applications in Wind Energy Forecasting

被引：2

作者：

Dhaka, Poonam ^{[1
]}

Sreejeth, Mini ^{[1
]}

Tripathi, M. M. ^{[1
]}

机构：

[1] Delhi Technol Univ, Elect Engn Dept, Delhi 110042, India

来源：

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING | 2024年

关键词：

PARTICLE SWARM OPTIMIZATION; RECURRENT NEURAL-NETWORKS; POWER PREDICTION; HYBRID MODEL; PERFORMANCE EVALUATION; VECTOR MACHINES; SPEED; ALGORITHM; DECOMPOSITION; REGRESSION;

D O I：

10.1007/s11831-024-10182-8

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Renewable energy forecasting, such as Wind and Solar forecasting, is becoming more critical as the demand for clean energy increases. Thus, it is crucial to enhance the accuracy of wind power predictions to ensure electrical energy system's efficient, reliable, and safe operation. Research on wind forecasting has increased dramatically over the past 10 years due to the success of Artificial Intelligence (AI) technologies like machine learning and deep learning. Despite their potential, AI approaches are fraught with uncertainties. It remains unclear how certain factors may influence the accuracy of AI algorithm predictions. This study reviews AI applications in Wind energy forecasting, aiming to provide an analysis of (1) AI-based structures and optimizers for Wind forecasting, (2) forecast performance evaluation for Deterministic and Probabilistic techniques.

引用

页码：4853 / 4878

页数：26

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