This chapter emphasizes that more attention has to be given to applying hybrid deep learning techniques to the PV power forecast in order to optimize renewable energy systems toward a greener environment. Proposed models integrate advanced pre-processing methods, and decomposition algorithms for time series data are developed based on fusing decomposition algorithms along with deep learning models in constructing a robust and accurate framework for the forecast. In this work, we further propose incorporating a transformer model along with variances of VMD to take the performance a step ahead with regard to the estimation of PV power. Results about three grid-connected PV plants located in the South-Saharian region of Algeria, obtained after releasing the locality of the proposed model, show its performance: in fact, the normalized RMSE values obtained for the studied photovoltaic plants range from 2 to 2.4%. In this respect, the chapter has developed a complete solution for the estimation of PV energy output under different environmental conditions, capitalizing on strengths from these methods and trying to advance the reliability of PV power forecast. The contribution shall, in turn, support efficient renewable energy resource management and add toward global clean energy goals. The dependence on this, of course, is an essentiality with which one looks at the context.