Enhanced deep-learning-based forecasting of solar photovoltaic generation for critical weather conditions

Abstract Solar photovoltaic energy generation due to its high potential is being adopted as one of the main power sources by many countries to mitigate their climate and electrical power issues. Hence accurate forecasting becomes important to make grid operations smoother, and for this purpose, modern-day artificial intelligence technologies can make a significant contribution. This study is an endeavor to target accurate forecasting for different weather conditions by using a simple recurrent neural network, long–short-term memory and gated recurrent unit-based hybrid model, and bidirectional gated recurrent unit. The experimental dataset has been acquired from Quaid-e-Azam Solar Park, Bahawalpur, Pakistan. This study observed that the bidirectional gated recurrent unit outperforms the hybrid model, whereas the simple recurrent neural network lags most in accuracy. The results confirm that the bidirectional gated recurrent unit technique can perform accurately in all critical weather types. Whereas the values of root-mean-square error, mean absolute error, and R-squared values also ensure the precision of the model for all weather conditions, and the best of these parameters for bidirectional gated recurrent unit observed are 0.0012, 0.212, and 0.99, respectively, for the overcast dataset.