Arithmetic optimization with hybrid deep learning algorithm based solar radiation prediction model

Kashif Irshad, Nazrul Islam, Abdullatif A. Gari, Salem Algarni, Talal Alqahtani, Binash Imteyaz

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Solar radiation affects extreme weather occurrences, as well as on the temperature and mean sea level. Therefore, precise studies and measurements of geographical and temporal variations of solar radiation are required. The development of deep learning and machine learning methods for developing solar radiation predictive models is gaining traction. Therefore, this paper presents an Arithmetic Optimization with Hybrid Deep Learning based Solar Radiation Prediction model. The presented AOHDL-SRP model follows a three-stage process: pre-processing, prediction, and hyperparameter optimization. Primarily, the AOHDL-SRP model involves the min–max normalization technique to normalize the input data to a uniform format. Besides, the presented AOHDL-SRP model applies HDL using a convolutional neural network with an attention-oriented long short-term memory (ALSTM) model. Finally, the arithmetic optimization algorithm (AOA) is applied for the hyperparameter optimization of the HDL model, and it assists in improving the predictive performance. The experimental validation of the AOHDL-SRP model is tested, and the results are lower than different expectations. The AOHDL-SRP model has demonstrated that it is superior to all other models by achieving the highest possible R2-score of 100% and MSE, RMSE, and MAE values of 0.18, 0.43, and 0.32, respectively.

Original languageEnglish
Article number103165
JournalSustainable Energy Technologies and Assessments
Volume57
DOIs
StatePublished - Jun 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd

Keywords

  • Forecasting model deep learning and hyperparameter tuning
  • Solar radiation
  • Time series models

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology

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