Comparison of Wavelet Artificial Neural Network, Wavelet Support Vector Machine, and Adaptive Neuro-Fuzzy Inference System Methods in Estimating Total Solar Radiation in Iraq

• Published in: Energies (Basel) Vol. 16; no. 2; p. 985
• Main Authors: Anupong, Wongchai, Jweeg, Muhsin Jaber, Alani, Sameer, Al-Kharsan, Ibrahim H., Alviz-Meza, Aníbal, Cárdenas-Escrocia, Yulineth
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-01-2023
• Subjects: Adaptive systems; Alternative energy sources; ANFIS; Energy; Fossil fuels; Fuzzy logic; Inference; Neural networks; Photovoltaic cells; Power plants; Solar energy; Solar power; Solar radiation; Support vector machines; WANN; WSVM; Iraq
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en16020985

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, R2, 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 R2 for the WANN and WSVM methods was 0.89 and 0.86, respectively. The value of R2 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 R2 value was 0.84 and 0.83 in Wasit and Dhi Qar stations, respectively.