Solar Radiation and Wind Speed Forecasting using Deep Learning Technique

被引：4

作者：

Islam, Md Mainul ^{[1
]}

Nagrial, Mahmood ^{[1
]}

Rizk, Jamal ^{[1
]}

Hellany, Ali ^{[1
]}

机构：

[1] Western Sydney Univ, Sch Engn Design & Built Environm, Locked Bag 1797, Penrith, NSW 2751, Australia

来源：

2021 IEEE ASIA-PACIFIC CONFERENCE ON COMPUTER SCIENCE AND DATA ENGINEERING (CSDE) | 2021年

关键词：

solar energy; wind energy; random forest technique; coot algorithm; ENERGY; OUTPUT; MODEL;

D O I：

10.1109/CSDE53843.2021.9718372

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Solar radiation and wind speed are the fundamental parameters for the design and operations of solar and wind energy systems. Renewable energy sources (RESs) are intermittent and dependent on different atmospheric parameters. Therefore, it is crucial to accurately forecast RESs, such as solar radiation and wind speed. In this study, a deep learning-based random forest technique is proposed to predict solar radiation and wind speed. A novel coot algorithm (CA) is suggested to optimize the number of decision trees of the random forest model, and the performance of the CA is compared with the existing particle swarm optimization (PSO) technique. The results show that the performance of CA is better than PSO.

引用

页数：6

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