The Application of Deep Learning Techniques for Solar Power Forecasting

被引：1

作者：

Al-Jaafreh, Tamer Mushal ^{[1
]}

Al-Odienat, Abdullah ^{[2
]}

机构：

[1] Mutah Univ, Kark, Jordan

[2] Mutah Univ, Elect Engn Dept, Kark, Jordan

来源：

2022 13TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION SYSTEMS (ICICS) | 2022年

关键词：

Machine Learning; Deep Learning; LSTM; Solar irradiation; Forecasting process; Solar energy;

D O I：

10.1109/ICICS55353.2022.9811182

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The future forecasting of the solar irradiation is becoming very essential. There are other elements (features) contribute to the forecasting process of the solar irradiation. These features are closely related to the amount of solar irradiation arriving from the sun. As the weather factors are related to each other in terms of influence, a wide range of features that are necessary to enter the i process are considered in this research. This paper investigate the effect of some atmospheric factors like Evapotranspiration and soil temperature using deep learning techniques, like LSTM algorithm. The results show that higher accuracy is achieved when new features related to solar irradiation were included in the forecasting process. To the best of authors' knowledge, these new features have not previously considered in literature. The RMSE value is 0.34 obtained with 16 features used in forecasting process.

引用

页码：214 / 219

页数：6

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