  Electricity is one of the mandatory commodities for mankind today. To address
challenges and issues in the transmission of electricity through the
traditional grid, the concepts of smart grids and demand response have been
developed. In such systems, a large amount of data is generated daily from
various sources such as power generation (e.g., wind turbines), transmission
and distribution (microgrids and fault detectors), load management (smart
meters and smart electric appliances). Thanks to recent advancements in big
data and computing technologies, Deep Learning (DL) can be leveraged to learn
the patterns from the generated data and predict the demand for electricity and
peak hours. Motivated by the advantages of deep learning in smart grids, this
paper sets to provide a comprehensive survey on the application of DL for
intelligent smart grids and demand response. Firstly, we present the
fundamental of DL, smart grids, demand response, and the motivation behind the
use of DL. Secondly, we review the state-of-the-art applications of DL in smart
grids and demand response, including electric load forecasting, state
estimation, energy theft detection, energy sharing and trading. Furthermore, we
illustrate the practicality of DL via various use cases and projects. Finally,
we highlight the challenges presented in existing research works and highlight
important issues and potential directions in the use of DL for smart grids and
demand response.
