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The integration of renewable energy generation presented an important development around the globe and conveys countless financial, commercial, and environmental assistance. However, the random and non-linear nature of solar power generation, the significant perception of solar system may challenge the comprehension and operation of the existing power system. Nowadays, a smart energy forecasting system is important for solar energy generation to provide high-quality energy for end-users to enhance the power system’s reliability and operation. Inspired by the performance and advancements of deep learning approaches in the field of energy, a CNN-assisted deep echo state network (CNN-DeepESN) with principal components analysis (PCA) method is proposed for generating short-term solar energy forecasting. In this study, we designed a hybrid deep learning method to utilized one-hour solar …