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Solar Radiation Prediction Using Satin Bowerbird Optimization with Modified Deep Learning

Sheren Sadiq Hasan1, Zainab Salih Agee2, Bareen Shamsaldeen Tahir3, Subhi R. M. Zeebaree4,*

1 ITM Department, Technical College of Administrative, Duhok Polytechnic University, Duhok, Iraq
2 Computer Science Department, College of Science, Nawroz University, Duhok, Iraq
3 Accounting Department, College of Adminstration and Economics, University of Duhok, Duhok, Iraq
4 Energy Eng. Department, Technical College of Engineering, Duhok Polytechnic University, Duhok, Iraq

* Corresponding Author: Subhi R. M. Zeebaree. Email: email

Computer Systems Science and Engineering 2023, 46(3), 3225-3238. https://doi.org/10.32604/csse.2023.037434

Abstract

Solar energy will be a great alternative to fossil fuels since it is clean and renewable. The photovoltaic (PV) mechanism produces sunbeams’ green energy without noise or pollution. The PV mechanism seems simple, seldom malfunctioning, and easy to install. PV energy productivity significantly contributes to smart grids through many small PV mechanisms. Precise solar radiation (SR) prediction could substantially reduce the impact and cost relating to the advancement of solar energy. In recent times, several SR predictive mechanism was formulated, namely artificial neural network (ANN), autoregressive moving average, and support vector machine (SVM). Therefore, this article develops an optimal Modified Bidirectional Gated Recurrent Unit Driven Solar Radiation Prediction (OMBGRU-SRP) for energy management. The presented OMBGRU-SRP technique mainly aims to accomplish an accurate and time SR prediction process. To accomplish this, the presented OMBGRU-SRP technique performs data preprocessing to normalize the solar data. Next, the MBGRU model is derived using BGRU with an attention mechanism and skip connections. At last, the hyperparameter tuning of the MBGRU model is carried out using the satin bowerbird optimization (SBO) algorithm to attain maximum prediction with minimum error values. The SBO algorithm is an intelligent optimization algorithm that simulates the breeding behavior of an adult male Satin Bowerbird in the wild. Many experiments were conducted to demonstrate the enhanced SR prediction performance. The experimental values highlighted the supremacy of the OMBGRU-SRP algorithm over other existing models.

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Cite This Article

APA Style
Hasan, S.S., Agee, Z.S., Tahir, B.S., Zeebaree, S.R.M. (2023). Solar radiation prediction using satin bowerbird optimization with modified deep learning. Computer Systems Science and Engineering, 46(3), 3225-3238. https://doi.org/10.32604/csse.2023.037434
Vancouver Style
Hasan SS, Agee ZS, Tahir BS, Zeebaree SRM. Solar radiation prediction using satin bowerbird optimization with modified deep learning. Comput Syst Sci Eng. 2023;46(3):3225-3238 https://doi.org/10.32604/csse.2023.037434
IEEE Style
S.S. Hasan, Z.S. Agee, B.S. Tahir, and S.R.M. Zeebaree "Solar Radiation Prediction Using Satin Bowerbird Optimization with Modified Deep Learning," Comput. Syst. Sci. Eng., vol. 46, no. 3, pp. 3225-3238. 2023. https://doi.org/10.32604/csse.2023.037434



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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