An Investigation into the Methods and Applications of Deep Learning in Smart Grid

EasyChair Preprint 8314

Abstract

The manuscript represents the state-of-the-art review of the deep learning methods for smart grid applications. This paper reviews novel applications of deep learning algorithms in smart grid. The Deep learning based three algorithms i.e., Long-Short Term Memory, recurrent neural network, convolution neural network found to be most useful in smart grid application. These algorithms are found to be most useful for forecasting, cyber-attack, anomaly detection and electricity theft in smart grid. This paper briefly surveys the most usable deep learning algorithms for making the smart is resilience, accurate, and safe. The review result shows that the mentioned deep learning algorithms give an excellent results over other deep learning algorithm. Therefore, these three algorithms are widely acceptable for the evaluation of smart grids.

Keyphrases: Convolution Neural Network, Long Short-Term Memory, Recurrent Neural Network, Smart Grid, deep learning

@booklet{EasyChair:8314,
  author    = {Rituraj Rituraj},
  title     = {An Investigation into the Methods and Applications of Deep Learning in Smart Grid},
  howpublished = {EasyChair Preprint 8314},
  year      = {EasyChair, 2022}}