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

arXiv: https://arxiv.org/abs/2101.08013 · HTML: https://mlsanity.com/paper/2101.08013/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 175 references that are in the map.
- First posted on arXiv 20 Jan 2021.

## Builds on (most cited first, 15 of 15)

- [Real-time Power System State Estimation and Forecasting via Deep Neural Networks](https://mlsanity.com/paper/1811.06146/) (2018)
- [Deep Learning for Time Series Forecasting: The Electric Load Case](https://mlsanity.com/paper/1907.09207/) (2019)
- [On-line Building Energy Optimization using Deep Reinforcement Learning](https://mlsanity.com/paper/1707.05878/) (2017)
- [Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning](https://mlsanity.com/paper/1811.02756/) (2018)
- [Cyber-Security in Smart Grid: Survey and Challenges](https://mlsanity.com/paper/1809.02609/) (2018)
- [A Survey of Machine Learning Methods for Detecting False Data Injection Attacks in Power Systems](https://mlsanity.com/paper/2008.06926/) (2020)
- [Optimal Demand Response Using Device Based Reinforcement Learning](https://mlsanity.com/paper/1401.1549/) (2014)
- [A Survey on Blockchain for Big Data: Approaches, Opportunities, and Future Directions](https://mlsanity.com/paper/2009.00858/) (2020)
- [A supervised-learning-based strategy for optimal demand response of an HVAC System](https://mlsanity.com/paper/1904.13304/) (2019)
- [Blockchain for Future Smart Grid: A Comprehensive Survey](https://mlsanity.com/paper/1911.03298/) (2019)
- [Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey](https://mlsanity.com/paper/1912.13203/) (2019)
- [A Deep Learning Framework for Short-term Power Load Forecasting](https://mlsanity.com/paper/1711.11519/) (2017)
- [A Hybrid Distribution Feeder Long-Term Load Forecasting Method Based on Sequence Prediction](https://mlsanity.com/paper/1812.04480/) (2018)
- [Adversarial FDI Attack against AC State Estimation with ANN](https://mlsanity.com/paper/1906.11328/) (2019)
- [Machine Learning in Generation, Detection, and Mitigation of Cyberattacks in Smart Grid: A Survey](https://mlsanity.com/paper/2010.00661/) (2020)

## Built on by (most cited first, 2 of 2)

- [Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles](https://mlsanity.com/paper/2501.15544/) (2025)
- [Calibrated uncertainty quantification for prosumer flexibility aggregation in ancillary service markets](https://mlsanity.com/paper/2601.14663/) (2026)

## Abstract (arXiv)

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.