Deep Learning for Intelligent Demand Response and Smart Grids: A Comprehensive Survey

TL;DR

This survey reviews how deep learning techniques are applied to enhance smart grid management, demand response, and energy efficiency by analyzing large-scale data from various sources.

Contribution

It provides a comprehensive overview of deep learning applications in smart grids, covering fundamentals, state-of-the-art methods, practical use cases, and future research directions.

Findings

Deep learning improves load forecasting accuracy.

DL enables better energy theft detection.

Use cases demonstrate practical benefits of DL in smart grids.

Abstract

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.