Daily solar radiation prediction using LSTM neural networks

The integration of solar energy with the smart grids and existing infrastructure makes it a cost-effective and environmentally-friendly solution to address the growing energy need. To make use of the potential of solar energy, several challenges such as the stability of generated energy and the supply-demand imbalance must be overcome. In this regard, an accurate forecast model for global solar radiation (GSR) can be useful for power generation planning and system reliability. The GSR estimate is regarded as the most significant and critical element in defining solar system characteristics, thus, it is crucial in predicting the generated energy. This work, therefore, employs long-short-term memory (LSTM) as a deep learning method to successfully estimate solar irradiance and capture the stochastic fluctuations. In this respect, the measurement data (from year 2021) obtained from the station installed in Dicle University (Turkey), Science and Technology Application and Research Centre (DUBTAM) were used, and the efficiency of the proposed method was evaluated. © 2022 IEEE.