The Convolutional Neural Network (CNN) methodologies have been a fundamental deep
learning solution to smart grid applications. It is essential to investigate and evaluate
the progress of this method in the smart grid. Consequently, a comprehensive investigation
with the aid of PRISMA had been conducted. The PRISMA standard queries including the
CNNs and its abbreviation forms of ConvNet or CNN reveal a significant increase in
the popularity of this deep learning method in smart grid applications. This research
identifies 2200 pieces of literature in the field. After considering the PRISMA guideline
the most relevant and fundamental application had been reduced to 46 documents where
the single and hybrid methods had been identified. The investigation showed that hybrid
methods delivered a better performance with higher accuracy. It is expected that more
hybrid methods will have emerged in the smart grid application.
