Comparing the Renewable Energy Technologies via Forecasting Approaches

Renewable energy continues to gain importance in energy systems. Renewable energy generation is mainly affected by environmental impacts. As a result of this, more complex energy forecasting models are needed in comparison to fossil sources. Renewable energy forecasting models are developed with different techniques. Since the renewable energies have different characteristics, the success of the forecasting techniques varies depending on the type of renewable energy. The chaotic nature of renewable energy defects the success of the forecasting results. Renewable energy generation data with wind energy and hydro energy were collected from Turkey’s renewable energy system. We have developed forecasting models with renewable energy generation with long short-term memory (LSTM) and gated recurrent unit (GRU) which are special kinds of deep learning techniques, multiple linear regressions, and polynomial regression. This study evaluates deep learning models and statistical models. It is quite important to compare and evaluate renewable energy prediction models. We evaluate the forecasting models using evaluation metrics. The models are compared with Mean Absolute Error (MAE) and Mean Square Error (MSE). This paper provides a renewable energy forecasting method based on forecasting models to explore its effect on wind energy and hydro energy.