A New Insight for Daily Solar Radiation Prediction by Meteorological Data Using an Advanced Artificial Intelligence Algorithm: Deep Extreme Learning Machine Integrated with Variational Mode Decomposition Technique

Meysam Alizamir (), Kaywan Othman Ahmed, Jalal Shiri, Ahmad Fakheri Fard, Sungwon Kim, Salim Heddam and Ozgur Kisi
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Meysam Alizamir: Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz 5166616471, Iran
Kaywan Othman Ahmed: Department of Civil Engineering, Faculty of Engineering, Tishk International University, Sulaimani 46001, Iraq
Jalal Shiri: Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz 5166616471, Iran
Ahmad Fakheri Fard: Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz 5166616471, Iran
Sungwon Kim: Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju 36040, Republic of Korea
Salim Heddam: Faculty of Science, Agronomy Department, Hydraulics Division, University 20 Août 1955, Route El Hadaik BP 26, Skikda 21000, Algeria
Ozgur Kisi: Department of Civil Engineering, Luebeck University of Applied Sciences, 23562 Lübeck, Germany

Sustainability, 2023, vol. 15, issue 14, 1-35

Abstract: Reliable and precise estimation of solar energy as one of the green, clean, renewable and inexhaustible types of energies can play a vital role in energy management, especially in developing countries. Also, solar energy has less impact on the earth’s atmosphere and environment and can help to lessen the negative effects of climate change by lowering the level of emissions of greenhouse gas. This study developed thirteen different artificial intelligence models, including multivariate adaptive regression splines (MARS), extreme learning machine (ELM), Kernel extreme learning machine (KELM), online sequential extreme learning machine (OSELM), optimally pruned extreme learning machine (OPELM), outlier robust extreme learning machine (ORELM), deep extreme learning machine (DELM), and their versions combined with variational mode decomposition (VMD) as integrated models (VMD-DELM, VMD-ORELM, VMD-OPELM, VMD-OSELM, VMD-KELM, and VMD-ELM), for solar radiation estimation in Kurdistan region, Iraq. The daily meteorological data from 2017 to 2018 were used to implement suggested artificial models at Darbandikhan and Dukan stations, Iraq. The input parameters included daily data for maximum temperature (MAXTEMP), minimum temperature (MINTEMP), maximum relative humidity (MAXRH), minimum relative humidity (MINRH), sunshine duration (SUNDUR), wind speed (WINSPD), evaporation (EVAP), and cloud cover (CLOUDCOV). The results show that the proposed VMD-DELM algorithm considerably enhanced the simulation accuracy of standalone models’ daily solar radiation prediction, with average improvement in terms of RMSE of 13.3%, 20.36%, 25.1%, 27.1%, 34.17%, 38.64%, and 48.25% for Darbandikhan station and 5.22%, 10.01%, 10.26%, 21.01%, 29.7%, 35.8%, and 40.33% for Dukan station, respectively. The outcomes of this study reveal that the VMD-DELM two-stage model performed superiorly to the other approaches in predicting daily solar radiation by considering climatic predictors at both stations.

Keywords: solar radiation; deep ELM; outlier robust extreme learning machine; optimally pruned extreme learning machine; variational mode decomposition; prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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