Solar radiation and wind speed forecasting using deep learning technique

Md Mainul Islam; Mahmood Nagrial; Jamal Rizk; Ali Hellany

doi:10.1109/CSDE53843.2021.9718372

Solar radiation and wind speed forecasting using deep learning technique

Md Mainul Islam
, Mahmood Nagrial
, Jamal Rizk
, Ali Hellany

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

6 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 7th IEEE International Conference on Sustainable Technology and Engineering (i-COSTE 2021) and the 8th IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE 2021), 8-10 December 2021, Brisbane, Australia
Publisher	IEEE
Number of pages	6
DOIs	https://doi.org/10.1109/CSDE53843.2021.9718372
Publication status	Published - 2021
Event	IEEE International Conference on Sustainable Technology and Engineering - Duration: 8 Dec 2021 → …

Conference

Conference	IEEE International Conference on Sustainable Technology and Engineering
Period	8/12/21 → …

Bibliographical note

Publisher Copyright:
© IEEE 2022.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Access to Document

10.1109/CSDE53843.2021.9718372

Cite this

Islam, M. M., Nagrial, M., Rizk, J., & Hellany, A. (2021). Solar radiation and wind speed forecasting using deep learning technique. In Proceedings of the 7th IEEE International Conference on Sustainable Technology and Engineering (i-COSTE 2021) and the 8th IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE 2021), 8-10 December 2021, Brisbane, Australia IEEE. https://doi.org/10.1109/CSDE53843.2021.9718372

Islam, Md Mainul ; Nagrial, Mahmood ; Rizk, Jamal et al. / Solar radiation and wind speed forecasting using deep learning technique. Proceedings of the 7th IEEE International Conference on Sustainable Technology and Engineering (i-COSTE 2021) and the 8th IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE 2021), 8-10 December 2021, Brisbane, Australia. IEEE, 2021.

@inproceedings{0cb11de001cc49d4bab8e71a93fb2fe3,

title = "Solar radiation and wind speed forecasting using deep learning technique",

abstract = "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.",

author = "Islam, \{Md Mainul\} and Mahmood Nagrial and Jamal Rizk and Ali Hellany",

note = "Publisher Copyright: {\textcopyright} IEEE 2022.; IEEE International Conference on Sustainable Technology and Engineering ; Conference date: 08-12-2021",

year = "2021",

doi = "10.1109/CSDE53843.2021.9718372",

language = "English",

booktitle = "Proceedings of the 7th IEEE International Conference on Sustainable Technology and Engineering (i-COSTE 2021) and the 8th IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE 2021), 8-10 December 2021, Brisbane, Australia",

publisher = "IEEE",

}

Islam, MM, Nagrial, M, Rizk, J & Hellany, A 2021, Solar radiation and wind speed forecasting using deep learning technique. in Proceedings of the 7th IEEE International Conference on Sustainable Technology and Engineering (i-COSTE 2021) and the 8th IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE 2021), 8-10 December 2021, Brisbane, Australia. IEEE, IEEE International Conference on Sustainable Technology and Engineering, 8/12/21. https://doi.org/10.1109/CSDE53843.2021.9718372

Solar radiation and wind speed forecasting using deep learning technique. / Islam, Md Mainul; Nagrial, Mahmood; Rizk, Jamal et al.
Proceedings of the 7th IEEE International Conference on Sustainable Technology and Engineering (i-COSTE 2021) and the 8th IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE 2021), 8-10 December 2021, Brisbane, Australia. IEEE, 2021.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

TY - GEN

T1 - Solar radiation and wind speed forecasting using deep learning technique

AU - Islam, Md Mainul

AU - Nagrial, Mahmood

AU - Rizk, Jamal

AU - Hellany, Ali

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

UR - https://hdl.handle.net/1959.7/uws:62688

U2 - 10.1109/CSDE53843.2021.9718372

DO - 10.1109/CSDE53843.2021.9718372

M3 - Conference Paper

BT - Proceedings of the 7th IEEE International Conference on Sustainable Technology and Engineering (i-COSTE 2021) and the 8th IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE 2021), 8-10 December 2021, Brisbane, Australia

PB - IEEE

T2 - IEEE International Conference on Sustainable Technology and Engineering

Y2 - 8 December 2021

ER -

Islam MM, Nagrial M, Rizk J , Hellany A. Solar radiation and wind speed forecasting using deep learning technique. In Proceedings of the 7th IEEE International Conference on Sustainable Technology and Engineering (i-COSTE 2021) and the 8th IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE 2021), 8-10 December 2021, Brisbane, Australia. IEEE. 2021 doi: 10.1109/CSDE53843.2021.9718372

Solar radiation and wind speed forecasting using deep learning technique

Abstract

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