Modelling of Daily Solar Energy System Prediction using Support Vector Machine for Oman
Solar energy is the most important renewable energy source, which could be utilized through photovoltaic or thermal systems. This paper aims to design and implement a machine learning technique called Support Vector Machine (SVM) for the management of energy generation based on experimental work. The SVM model is consist of two inputs and one output. The inputs are solar radiation, and ambient temperature; the output is the photovoltaic 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. The experiments achieved a value of (R squared) equal to 0.0774 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.