Comparative study of the forecasting solar energy generation in Istanbul

Abstract

The importance of renewable energy sources makes it extremely important day by day due to the limited reserves of fossil fuels and the damage they cause to the environment. Fossil fuels play an important role in electrical energy production. This situation brings up the necessity of turning to alternative sources that cause the formation of renewable energies such as solar energy, which is one of the renewable energy sources. Solar energy is a renewable energy source with benefits such as ease of installation and use, as well as the fact that it does not pollute the environment or produce toxic waste. In the world and in our country, investments in solar power plants are increasing rapidly from year to year. In this study, the solar energy situation of our country was discussed and a model for solar energy generation forecasting was proposed by using RNN, LSTM, and GRU deep learning architecture using the İkitelli Solar Power Plant daily data of Istanbul between May 2018 and May 2019. Generation forecasting values for 5 days later were estimated with 0.0069 error and 0.92 R2 values, which are accepted as one of the most important performance criteria by the LSTM model. The LSTM model’s solar energy generation values are slightly greater than those of the other models, it can be concluded that the LSTM model is appropriate for forecasting solar energy generation.