This paper comparatively analyzes different deep learning algorithms in PV production and energy consumption forecasting. The performance analysis of different architectures—GRU, CNN, LSTM, BI-LSTM, CLSTM, CNN-LSTM, CNN-biLSTM, and combined models—against several evaluation metrics, including MSE, RMSE, and MAE, have been presented. The analysis shows the advantages and limitations of each model, while strong performance was observed in BI-LSTM and CLSTM for the capture of temporal and spatial features, respectively. Meanwhile, the hybrid models are more accurate in prediction. It is enhanced in this work to consider its importance in establishing the balance between the requirement of predictive accuracy and computational efficiency. Without accurate forecasting of PV production and energy consumption, efficient grid management, optimal resource allocation, and planning of energy cannot be made. A further study on the optimization of hybrid models should be conducted toward reducing computational complexity and integrating emerging deep learning techniques to enhance forecasting capability.