LiPSG: Lightweight Privacy-Preserving Q-Learning-Based Energy Management for the IoT-Enabled Smart Grid

Wang, Zhuzhu; Liu, Yang; Ma, Zhuo; Liu, Ximeng; Ma, Jianfeng

doi:10.1109/jiot.2020.2968631

Cited by 54 publications

(28 citation statements)

References 37 publications

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“…Figure 4 lists the commonly used RL algorithms. Q-learning and SARSA (state-action-reward-state-action) are used in attack detection [52] and energy management [42,53]. Deep reinforcement learning (DRL) is an algorithm that combines the perception of DL with the decision making of RL.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Artificial Intelligence Techniques in Smart Grid: A Survey

2021

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The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.

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Section: Reinforcement Learningmentioning

confidence: 99%

Artificial Intelligence Techniques in Smart Grid: A Survey

2021

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“…That means the identity of consumers alone is not sufficient to provide a public and secret key. In [ 41 ], a privacy-preserving architecture is suggested for the smart grid using a Q-learning-based optimised approach. It exploits the cryptography technique to outsource multiregional electricity data securely.…”

Section: Industrial Iot System Integritymentioning

confidence: 99%

Internet of Things for System Integrity: A Comprehensive Survey on Security, Attacks and Countermeasures for Industrial Applications

Abosata

Al‐Rubaye

Tsourdos

et al. 2021

Sensors

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The growth of the Internet of Things (IoT) offers numerous opportunities for developing industrial applications such as smart grids, smart cities, smart manufacturers, etc. By utilising these opportunities, businesses engage in creating the Industrial Internet of Things (IIoT). IoT is vulnerable to hacks and, therefore, requires various techniques to achieve the level of security required. Furthermore, the wider implementation of IIoT causes an even greater security risk than its benefits. To provide a roadmap for researchers, this survey discusses the integrity of industrial IoT systems and highlights the existing security approaches for the most significant industrial applications. This paper mainly classifies the attacks and possible security solutions regarding IoT layers architecture. Consequently, each attack is connected to one or more layers of the architecture accompanied by a literature analysis on the various IoT security countermeasures. It further provides a critical analysis of the existing IoT/IIoT solutions based on different security mechanisms, including communications protocols, networking, cryptography and intrusion detection systems. Additionally, there is a discussion of the emerging tools and simulations used for testing and evaluating security mechanisms in IoT applications. Last, this survey outlines several other relevant research issues and challenges for IoT/IIoT security.

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“…By implementing distributed and renewable energy sources, the balance between demand, power supply, and the quality of electricity in the electricity network is becoming increasingly difficult 1 . Because the traditional grid is not designed for bidirectional electricity flow, the electricity networks have difficulties in identifying power increase the transparency from solar and wind power sources 2 .…”

Section: Outline About the Renewable Energy Sourcesmentioning

confidence: 99%

Machine learning‐based energy efficient technologies for smart grid

Yao

Li²,

Zuo³

et al. 2021

Int Trans Electr Energ Syst

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The smart grid will allow substantial electricity savings and peak demand savings by potentially supplying utility power for direct load management, the calculation in support of competitive pricing, and even the granular data required for energy usage to be more targeted explicitly at customer needs, the processing of data and predictions for a smart grid in a building with the energy profile and occupants' profile, is challenging. This article has been suggested a Machine Learning-Based Energy-Efficient Framework to analyze the motion of occupants, deliver short-term energy forecasts, and assign renewable energy in the smart grid. Second, an indoor localization device with wireless data analysis collects occupants' profile, and the energy profile is managed by a real-time smart meter network with an electrical charge evaluation. Furthermore, the energy profile and 24-hour profile will be paired with a forecast utilizing an online machine learning framework with an analysis of data in real-time. To decrease peak demand for the primary power grid, the solar energy source is assigned to the further power grids based upon the forecast occupant movement profile and energy consumption profile. On the smart gateway network, the complete power flow with minimal computing resources and a general enabled engine can be controlled. The results of the studies on real-time datasets show that the precision of the suggested energy forecast will increase significantly when compared to the other existing methods.

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LiPSG: Lightweight Privacy-Preserving Q-Learning-Based Energy Management for the IoT-Enabled Smart Grid

Cited by 54 publications

References 37 publications

Artificial Intelligence Techniques in Smart Grid: A Survey

Artificial Intelligence Techniques in Smart Grid: A Survey

Internet of Things for System Integrity: A Comprehensive Survey on Security, Attacks and Countermeasures for Industrial Applications

Machine learning‐based energy efficient technologies for smart grid

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