Open Access

ARTICLE

Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

by Abdennasser Dahmani¹, Yamina Ammi², Nadjem Bailek^3,4,*, Alban Kuriqi^5,6, Nadhir Al-Ansari^7,*, Salah Hanini², Ilhami Colak⁸, Laith Abualigah^{9,10,11,12,13,14}, El-Sayed M. El-kenawy¹⁵

1 Department of Mechanical Engineering, Faculty of Science and Technology, GIDD Industrial Engineering and Sustainable Development Laboratory, University of Relizane, Bourmadia, Relizane, 48000, Algeria
2 Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Medea, Urban Pole, Medea, 26000, Algeria
3 Energies and Materials Research Laboratory, Faculty of Sciences and Technology, University of Tamanghasset, Tamanrasset, 10034, Algeria
4 Sustainable Development and Computer Science Laboratory, Faculty of Sciences and Technology, Ahmed Draia University of Adrar, Adrar, Algeria
5 CERIS, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, 1049-001, Portugal
6 Civil Engineering Department, University for Business and Technology, Pristina, 10000, Kosovo
7 Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea, 97187, Sweden
8 Engineering and Architecture Faculty, Nisantasi University, Istanbul, Turkey
9 Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq, 25113, Jordan
10 Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan
11 MEU Research Unit, Middle East University, Amman, 11831, Jordan
12 Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan
13 School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, 11800, Malaysia
14 Department of Computing and Information Systems, School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya, 27500, Malaysia
15 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, 35712, Egypt

* Corresponding Authors: Nadjem Bailek. Email: ; Nadhir Al-Ansari. Email:

(This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)

Computers, Materials & Continua 2023, 77(2), 2579-2594. https://doi.org/10.32604/cmc.2023.040625

Received 25 March 2023; Accepted 01 July 2023; Issue published 29 November 2023

Abstract

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil, gas, and uranium are rapidly running out. Research into renewable energy sources such as solar energy is being pursued to counter this. Solar energy is one of the most promising renewable energy sources, as it has the potential to meet the world’s energy needs indefinitely. This study aims to develop and evaluate artificial intelligence (AI) models for predicting hourly global irradiation. The hyperparameters were optimized using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton training algorithm and STATISTICA software. Data from two stations in Algeria with different climatic zones were used to develop the model. Various error measurements were used to determine the accuracy of the prediction models, including the correlation coefficient, the mean absolute error, and the root mean square error (RMSE). The optimal support vector machine (SVM) model showed exceptional efficiency during the training phase, with a high correlation coefficient (R = 0.99) and a low mean absolute error (MAE = 26.5741 Wh/m²), as well as an RMSE of 38.7045 Wh/m² across all phases. Overall, this study highlights the importance of accurate prediction models in the renewable energy, which can contribute to better energy management and planning.

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