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Comparative analysis of solar power generation prediction system using deep learning

Due to the seriousness of environmental pollution, modern society is making efforts to switch from fossil fuel-centered energy to new and renewable energy worldwide. Among these new and renewable energies, solar energy is currently attracting attention due to its high growth potential. Among the renewable energy currently used, solar energy can generate a large amount of power without air pollutants and is widely used due to its high utilization. However, it is difficult to accurately predict the amount of power generation because solar energy, which is the source of energy from the sun, is greatly affected by seasons, weather, and installation environment. Therefore, this paper compares and analyzes the prediction and accuracy of solar power generation according to the environment through weather forecasts provided by the Meteorological Administration, using Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) learning models.