Non-cooperative differential game based energy consumption control for dynamic demand response in smart grid

Wang, Manxi; Xu, Haitao; Yang, Shengsong; Yang, Lifeng; Duan, Ruifeng; Zhou, Xianwei

doi:10.23919/jcc.2019.08.010

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…Machine learning is an artificial intelligence branch, in which machines automatically learn from data without direct human involvement 25 . Machine learning algorithms represent techniques that seek patterns and interactions within the data and construct a system that predicts these patterns and interrelations 26,27 . Intelligent decisions can be taken, and unexpected shifts in consumer needs, power outages, suddenly dropping.…”

Section: Outline About the Renewable Energy Sourcesmentioning

confidence: 99%

“…25 Machine learning algorithms represent techniques that seek patterns and interactions within the data and construct a system that predicts these patterns and interrelations. 26,27 Intelligent decisions can be taken, and unexpected shifts in consumer needs, power outages, suddenly dropping. Renewable energy production rises, or some other catastrophic occurrence will arise from machine intelligence and artificial intelligence in the Smart Grid.…”

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confidence: 99%

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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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Section: Outline About the Renewable Energy Sourcesmentioning

confidence: 99%

mentioning

confidence: 99%

Machine learning‐based energy efficient technologies for smart grid

Yao

Li²,

Zuo³

et al. 2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…As a key bearer for the integration and coordination of operation technology and information and communication technology, fog computing needs to support coordination in connection, data, management, control, application, security, etc. [4][5][6][7][8][9]. The first step is to build the Beidou-1 satellite navigation system (also known as the Beidou satellite navigation test system).…”

Section: The Meaning Of Fog Computingmentioning

confidence: 99%

“…At present, the concept of big data has gradually become popular. At home and abroad, all walks of life attach great importance to the development of big data and related basic theory and technology research and closely combined with various application fields [4] have set up major projects and supporting policies to promote the development of big data. There are various typical applications such as traffic big data, financial big data, and medical big data.…”

Section: Introductionmentioning

confidence: 99%

Development and Application of Big Data in the Field of Satellite Navigation

Zhang

2021

Wireless Communications and Mobile Computing

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Based on the characteristics of big data, the meaning of fog computing, and the spatiotemporal data characteristics of satellite navigation systems, the concept, connotation, and characteristics of Beidou big data were put forward. The Beidou big data processing process was sorted out, the preliminary architecture of the Beidou big data system with fog computing function was built, and finally the fog computing based Beidou big data system was proposed. Big data research content provides technical support for further tapping the value of Beidou satellite navigation system data and realizing the incremental development of satellite navigation systems.

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“…For the context-aware tasks with individual priorities, in order to allocate service resources, we assume that there is a fog computing server in the individual priority groups to control the allocated service resources [19,20]. The fog computing server has enough computing resources to calculate and allocate the appropriate resources for the context-aware tasks based on the task priorities.…”

Section: System Modelmentioning

confidence: 99%

Dynamic Priority-Based Service Resource Allocation for Context-Aware Conflict Resolution in Wisdom Network with Fog Computing

Lin

et al. 2020

Wireless Communications and Mobile Computing

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With the development of wisdom network, this paper assumes that intelligent devices become more and more intelligent, which can easily collect and provide a variety of context awareness data. The research goal is to design a dynamic conflict resolution strategy for context-aware resource allocation. The limited availability of resources inevitably leads to conflicts. Considering the characteristics of wisdom network, the quality of service when solving conflicts, a mechanism is proposed to improve the quality of services and to solve the resources allocation conflicts. This paper constructs the optimal model of context-aware based on a differential game and optimizes the resource allocation of context-aware based on the priority of scenarios. Fog computing is used to provide enough computing resources for the control of resource allocation of context-aware. The Bellman dynamic programming is introduced to solve the feedback Nash equilibrium solution of the proposed differential game model, to obtain the optimal allocation of service resources and solve the effectiveness of resource allocation.

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Non-cooperative differential game based energy consumption control for dynamic demand response in smart grid

Cited by 8 publications

References 4 publications

Machine learning‐based energy efficient technologies for smart grid

Machine learning‐based energy efficient technologies for smart grid

Development and Application of Big Data in the Field of Satellite Navigation

Dynamic Priority-Based Service Resource Allocation for Context-Aware Conflict Resolution in Wisdom Network with Fog Computing

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