mlsanityData to 7 Oct 2026

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

arXiv 2101.08013 ↗First posted 20 Jan 2021cs.LG cs.SY eess.SYcited by 2

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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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 175 references that are in the map. First posted on arXiv 20 Jan 2021.

Builds on

15 of 175 references.

References · 15

  1. 2018Real-time Power System State Estimation and Forecasting via Deep Neural Networkscited by 32
  2. 2019Deep Learning for Time Series Forecasting: The Electric Load Casecited by 31
  3. 2017On-line Building Energy Optimization using Deep Reinforcement Learningcited by 27
  4. 2018Bayesian State Estimation for Unobservable Distribution Systems via Deep Learningcited by 14
  5. 2018Cyber-Security in Smart Grid: Survey and Challengescited by 12
  6. 2020A Survey of Machine Learning Methods for Detecting False Data Injection Attacks in Power Systemscited by 8
  7. 2014Optimal Demand Response Using Device Based Reinforcement Learningcited by 7
  8. 2020A Survey on Blockchain for Big Data: Approaches, Opportunities, and Future Directionscited by 5
  9. 2019A supervised-learning-based strategy for optimal demand response of an HVAC Systemcited by 4
  10. 2019Blockchain for Future Smart Grid: A Comprehensive Surveycited by 4
  11. 2019Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Surveycited by 4
  12. 2017A Deep Learning Framework for Short-term Power Load Forecastingcited by 3
3 more
  1. 2018A Hybrid Distribution Feeder Long-Term Load Forecasting Method Based on Sequence Predictioncited by 3
  2. 2019Adversarial FDI Attack against AC State Estimation with ANNcited by 3
  3. 2020Machine Learning in Generation, Detection, and Mitigation of Cyberattacks in Smart Grid: A Surveycited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehiclescited by 2
  2. 2026Calibrated uncertainty quantification for prosumer flexibility aggregation in ancillary service marketscited by 0