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.