Modeling of Daily Solar Energy System Prediction using Soft Computing Methods for Oman

Abstract

The aim of this study is to design and implement soft computing techniques called Support Vector Machine (SVM) and Multilayer Perceptron (MLP) for great management of energy generation based on experimental work. Solar energy could be utilized through thermal systems or Photovoltaics (PV) and it is renewable energy source, environmental friendly and proven globally for a long time. The SVM and MLP models are consist of two inputs layers and one layer output. The inputs of SVM network are solar radiation and time, while the output is the PV current. The inputs of MLP network are solar radiation and ambient temperature, while the output is the PV current. The practical implementation of the proposed SVM model is achieved a final MSE of (0.026378744) in training phase and (0.035615759) in cross validation phase. Besides, MLP is achieved a final MSE of (0.005804253) in the training phase and it is achieved (0.010523501) in cross validation phase. The final MSE of cross validation with standard deviation is (0.000527668). The experiments achieved in the predicting model a value of determination factor (R² = 0.9844388787) for SVM and (R² = 0.9701310549) for MLP which indicates the predicting model is very close to the regression line and a well data fitting to the statistical model. Besides, the proposed model achieved less MSE in comparison with other related work.