Comparison of Wavelet Artificial Neural Network, Wavelet Support Vector Machine, and Adaptive Neuro-Fuzzy Inference System Methods in Estimating Total Solar Radiation in Iraq
Estimating the amount of solar radiation is very important in evaluating the amount of energy that can be received from the sun for the construction of solar power plants. Using machine learning tools to estimate solar energy can be a helpful method. With a high number of sunny days, Iraq has a high...
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MDPI AG
2023-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/2/985 |
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| author | Wongchai Anupong Muhsin Jaber Jweeg Sameer Alani Ibrahim H. Al-Kharsan Aníbal Alviz-Meza Yulineth Cárdenas-Escrocia |
| author_facet | Wongchai Anupong Muhsin Jaber Jweeg Sameer Alani Ibrahim H. Al-Kharsan Aníbal Alviz-Meza Yulineth Cárdenas-Escrocia |
| author_sort | Wongchai Anupong |
| collection | DOAJ |
| description | Estimating the amount of solar radiation is very important in evaluating the amount of energy that can be received from the sun for the construction of solar power plants. Using machine learning tools to estimate solar energy can be a helpful method. With a high number of sunny days, Iraq has a high potential for using solar energy. This study used the Wavelet Artificial Neural Network (WANN), Wavelet Support Vector Machine (WSVM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) techniques to estimate solar energy at Wasit and Dhi Qar stations in Iraq. RMSE, EMA, R<sup>2</sup>, and IA criteria were used to evaluate the performance of the techniques and compare the results with the actual measured value. The results showed that the WANN and WSVM methods had similar results in solar energy modeling. However, the results of the WANN technique were slightly better than the WSVM technique. In Wasit and Dhi Qar stations, the value of R<sup>2</sup> for the WANN and WSVM methods was 0.89 and 0.86, respectively. The value of R<sup>2</sup> in the WANN and WSVM methods in Wasit and Dhi Qar stations was 0.88 and 0.87, respectively. The ANFIS technique also obtained acceptable results. However, compared to the other two techniques, the ANFIS results were lower, and the R<sup>2</sup> value was 0.84 and 0.83 in Wasit and Dhi Qar stations, respectively. |
| first_indexed | 2024-03-09T12:50:32Z |
| format | Article |
| id | doaj.art-f8b12ccab6164bb988ce4ce897461f79 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T12:50:32Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-f8b12ccab6164bb988ce4ce897461f792023-11-30T22:07:51ZengMDPI AGEnergies1996-10732023-01-0116298510.3390/en16020985Comparison of Wavelet Artificial Neural Network, Wavelet Support Vector Machine, and Adaptive Neuro-Fuzzy Inference System Methods in Estimating Total Solar Radiation in IraqWongchai Anupong0Muhsin Jaber Jweeg1Sameer Alani2Ibrahim H. Al-Kharsan3Aníbal Alviz-Meza4Yulineth Cárdenas-Escrocia5Department of Agricultural Economy and Development, Faculty of Agriculture, Chiang Mai University, Chiang Mai 50200, ThailandCollege of Technical Engineering, Al-Farahidi University, Baghdad 10001, IraqThe University of Mashreq, Baghdad 10001, IraqComputer Technical Engineering Department, College of Technical Engineering, The Islamic University, Najaf 54001, IraqGrupo de Investigación en Deterioro de Materiales, Transición Energética y Ciencia de datos DANT3, Facultad de Ingenieria y Urbanismo, Universidad Señor de Sipán, Km 5 Via Pimentel, Chiclayo 14001, PeruGIOPEN, Energy Optimization Research Group, Energy Department, Universidad de la Costa (CUC), Cl. 58 ##55-66, Barranquilla 080016, Atlántico, ColombiaEstimating the amount of solar radiation is very important in evaluating the amount of energy that can be received from the sun for the construction of solar power plants. Using machine learning tools to estimate solar energy can be a helpful method. With a high number of sunny days, Iraq has a high potential for using solar energy. This study used the Wavelet Artificial Neural Network (WANN), Wavelet Support Vector Machine (WSVM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) techniques to estimate solar energy at Wasit and Dhi Qar stations in Iraq. RMSE, EMA, R<sup>2</sup>, and IA criteria were used to evaluate the performance of the techniques and compare the results with the actual measured value. The results showed that the WANN and WSVM methods had similar results in solar energy modeling. However, the results of the WANN technique were slightly better than the WSVM technique. In Wasit and Dhi Qar stations, the value of R<sup>2</sup> for the WANN and WSVM methods was 0.89 and 0.86, respectively. The value of R<sup>2</sup> in the WANN and WSVM methods in Wasit and Dhi Qar stations was 0.88 and 0.87, respectively. The ANFIS technique also obtained acceptable results. However, compared to the other two techniques, the ANFIS results were lower, and the R<sup>2</sup> value was 0.84 and 0.83 in Wasit and Dhi Qar stations, respectively.https://www.mdpi.com/1996-1073/16/2/985solar energyWANNWSVMANFIS |
| spellingShingle | Wongchai Anupong Muhsin Jaber Jweeg Sameer Alani Ibrahim H. Al-Kharsan Aníbal Alviz-Meza Yulineth Cárdenas-Escrocia Comparison of Wavelet Artificial Neural Network, Wavelet Support Vector Machine, and Adaptive Neuro-Fuzzy Inference System Methods in Estimating Total Solar Radiation in Iraq Energies solar energy WANN WSVM ANFIS |
| title | Comparison of Wavelet Artificial Neural Network, Wavelet Support Vector Machine, and Adaptive Neuro-Fuzzy Inference System Methods in Estimating Total Solar Radiation in Iraq |
| title_full | Comparison of Wavelet Artificial Neural Network, Wavelet Support Vector Machine, and Adaptive Neuro-Fuzzy Inference System Methods in Estimating Total Solar Radiation in Iraq |
| title_fullStr | Comparison of Wavelet Artificial Neural Network, Wavelet Support Vector Machine, and Adaptive Neuro-Fuzzy Inference System Methods in Estimating Total Solar Radiation in Iraq |
| title_full_unstemmed | Comparison of Wavelet Artificial Neural Network, Wavelet Support Vector Machine, and Adaptive Neuro-Fuzzy Inference System Methods in Estimating Total Solar Radiation in Iraq |
| title_short | Comparison of Wavelet Artificial Neural Network, Wavelet Support Vector Machine, and Adaptive Neuro-Fuzzy Inference System Methods in Estimating Total Solar Radiation in Iraq |
| title_sort | comparison of wavelet artificial neural network wavelet support vector machine and adaptive neuro fuzzy inference system methods in estimating total solar radiation in iraq |
| topic | solar energy WANN WSVM ANFIS |
| url | https://www.mdpi.com/1996-1073/16/2/985 |
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