SOLAR RADIATION FORECAST by USING MACHINE LEARNING METHOD for GAZIANTEP PROVINCE

Authors : Yusuf Alper KAPLAN, Emre BATUR, Gülizar Gizem ÜNALDI

Pages : 127-135

View : 19 | Download : 7

Publication Date : 2022-12-31

Article Type : Research Paper

Abstract :Renewable energy sources have become a popular topic all over the world in terms of cost, efficiency and environmental pollution. Solar energy is the most significant of the renewable energy sources. Solar energy, which was used only as heat and light energy in the past, is widely used in electrical energy production with the advancement of today\`s technology. Traditionally used photovoltaic cells are semiconductor materials that are produced in various chemical structures and convert the energy they receive from sunlight directly into electrical energy. The research and development of photovoltaic cells is moving forward at an accelerating pace. With this development process and relying on the today\`s technology, it is aimed to increase the efficiency of photovoltaic cells and to produce more electrical energy as a result of various trials. By analysing the energy production of photovoltaic cells, efficiency-enhancing situations are examined according to solar radiation values. In this study, a model was constructed using the regression approach, which is a method of machine learning. This model has been developed using the MATLAB program of the meteorological data of 2021 from Gaziantep. In addition, a variety of error analysis tests were utilized in order to evaluate the effectiveness of the model that was built. As a consequence, the model created using the linear regression method yields successful results in estimating solar radiation in Gaziantep province. This is demonstrated by the coefficient of determination insert ignore into journalissuearticles values(R2); value of 0.98, the Mean Absolute Error insert ignore into journalissuearticles values(MAE); value of 0.023, the Root Mean Square Error insert ignore into journalissuearticles values(RMSE); value of 0.028, and the Mean Square Error insert ignore into journalissuearticles values(MSE); value of 0.0008.
Keywords : Solar energy, Energy production, Photovoltaic cell, Cell efficiency, Machine learning