Abstract: As the demand for energy rises significantly in all countries, Photovoltaic power generation has thus increased significantly in popularity and demand as it offers clean and accessible energy. Nonetheless, the challenge of solar power is in its instability as it depends significantly on weather variables. Thus, forecasting day-ahead PV power generation is essential for smart grid systems scheduling and operation and solar energy providers. This thesis aims to establish the relationship between the meteorological variables that mostly affect solar power generation in the kingdom of Bahrain. Secondly, investigating other ML models that have not been deployed in past literature to forecast the PV power output, which are GRU, Bi-LSTM, and Bi-GRU. In this thesis, the Speannan Corelation Coefficient was used to discover the strength of 15 meteorological attributes with the PV power output, which were seven variables. Using a simple vanilla structure and a hybrid structure containing one layer of CNN, the models were tested and compared against the LSTM. To fiuther test the model's ability to accurately predict on less historical data, the five-month dataset was split into three and two months that capture the winter and summer seasons, respectively. The tests were run using data collected from the renewable energy lab at UoB. After analyzing the results, it was concluded that LSTM gave the worst performance across all datasets using both structures, the simple and hybrid. The hybrid form enhanced the performance most significantly in the LSTM than the other models, nonetheless still producing the highest RMSE and MAE. The CNN-GRU and CNN-Bi-LSTM gave low RMSE and MAE when trained on the summer data in contrast to CNN-Bi-GRU, which worsened radically, proving robust to provide reliable and accurate predictions on less historical data. Although overall results were comparable between GRU, Bi-LSTM, and Bi-GRU, both in hybrid and vanilla structures, the best model that gave the highest accuracy for the five-month dataset was CNN-Bi-GRU with RMSE 20.74 and MAE 9.64. As for summer and winter data, the CNN-Bi-LSTM gave the best performance for both, with RMSE of 8.17, 21.72, and MAE of 4.23 and 10.32, respectively. Keywords: Photovoltaic Power Output, Renewable Energy, Solar Power Generation, Machine Leaming Algorithms, Time Series Forecast, GRU, Bi-GRU, Bi-LSTM, LSTM