Adaptive DE Algorithm for Novel Energy Control Framework Based on Edge Computing in IIoT Applications

Xu, Zhengwei; Han, Guangjie; Zhu, Hongbo; Liu, Li; Guizani, Mohsen

doi:10.1109/tii.2020.3007644

Cited by 20 publications

(7 citation statements)

References 19 publications

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“…Liu et al (2019) offered an edge computing infrastructure based on deep reinforcement learning, applied the algorithm to the physical network energy management system, and proved the method's effectiveness (Liu et al, 2019). Xu et al (2020) proposed an efficient energy control framework supported by edge computing to reduce energy waste and increase the interests of industrial users. The best load mode and corresponding ES capacity of the battery ES system can be obtained through this framework.…”

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confidence: 99%

RETRACTED: The energy storage and optimal dispatch supply chain for new energy grids using edge computing and the internet of things

Liu

2023

Expert Systems

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The intermittent and uncertainty of new energy in the grid connection process affects the overall quality of the grid. To resolve the scattered geographical locations, small individual capacities and poor controllability of distributed energy storage (DES) devices, edge computing is applied in conjunction with aggregate service providers to build an edge computing‐based aggregate model of DES. Regarding its characteristics, the schedulable potential of the energy storage device is analysed through modelling. A multi‐objective scheduling model with multiple parties participating in the subject's behaviour is established using the Internet of Things (IoT) optimization strategy. The simulation example of concrete data further verifies the validity of the model. Results demonstrate that the edge computing‐based aggregate model of DES effectively reduces the calculation load's peak‐valley load and reduces the wind power abandonment. In the meantime, the multiple starts and stops of the thermal power unit reduce the operation cost of the unit. The multi‐objective dispatch model can reduce the opportunity cost and payment of DES effectively. This model achieves load peak reduction and valley filling and reduces the peak dispatch cost of the power grid. The research results can provide some ideas for storing and utilizing the new energy.

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confidence: 99%

RETRACTED: The energy storage and optimal dispatch supply chain for new energy grids using edge computing and the internet of things

Liu

2023

Expert Systems

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“…If the class label in the clustering result is the same as the class label of the original data, the clustering is correct. The correct number of samples in clustering is denoted as R, and the total number of data as T [39]. The formula for calculating accuracy is…”

Section: Methodsmentioning

confidence: 99%

A Novel DBSCAN Clustering Algorithm via Edge Computing‐Based Deep Neural Network Model for Targeted Poverty Alleviation Big Data

Liu

Qin

et al. 2021

Wireless Communications and Mobile Computing

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Big data technology has been developed rapidly in recent years. The performance improvement mechanism of targeted poverty alleviation is studied through the big data technology to further promote the comprehensive application of big data technology in poverty alleviation and development. Using the data mining knowledge to accurately identify the poor population under the framework of big data, compared with the traditional identification method, it is obviously more accurate and persuasive, which is also helpful to find out the real causes of poverty and assist the poor residents in the future. In the current targeted poverty alleviation work, the identification of poor households and the matching of assistance measures are mainly through the visiting of village cadres and the establishment of documents. Traditional methods are time-consuming, laborious, and difficult to manage. It always omits lots of useful family information. Therefore, new technologies need to be introduced to realize intelligent identification of poverty-stricken households and reduce labor costs. In this paper, we introduce a novel DBSCAN clustering algorithm via the edge computing-based deep neural network model for targeted poverty alleviation. First, we deploy an edge computing-based deep neural network model. Then, in this constructed model, we execute data mining for the poverty-stricken family. In this paper, the DBSCAN clustering algorithm is used to excavate the poverty features of the poor households and complete the intelligent identification of the poor households. In view of the current situation of high-dimensional and large-volume poverty alleviation data, the algorithm uses the relative density difference of grid to divide the data space into regions with different densities and adopts the DBSCAN algorithm to cluster the above result, which improves the accuracy of DBSCAN. This avoids the need for DBSCAN to traverse all data when searching for density connections. Finally, the proposed method is utilized for analyzing and mining the poverty alleviation data. The average accuracy is more than 96%. The average F -measure, NMI, and PRE values exceed 90%. The results show that it provides decision support for precise matching and intelligent pairing of village cadres in poverty alleviation work.

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“…After the 1990s, the study of the relationship between rural development and the media in development communication entered a new phase, revolving around the question of "how much and what role does communication play in rural modernization" [6]. Development is a systemic process, and if communication is useless or even counterproductive for some components, it will inevitably threaten the smooth functioning of the entire development system [7]. During this period, much attention was paid to the role of communication in social development, and it was argued that, as media technologies were updated, peasants entering the cities were also applying flexible and varied communication channels [8].…”

Section: Related Workmentioning

confidence: 99%

Research on the Construction Model of 5G Special Town Based on Edge Computing

Pengchao

2022

Scientific Programming

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This study conducts a study on edge computing technology, focusing on the advantages of edge computing technology and its applications, and delves into the edge computing architecture, which leads to an in-depth study on the construction of a 5G special town model. Based on the existing 5G characteristic town construction model, this study proposes a new model of edge computing and proposes an application partitioning algorithm based on this model by analyzing the edge computing scheduling scenarios to minimize the total time cost of the system. By analyzing the edge computing scheduling scenarios, an application partitioning algorithm based on this model is proposed, so that the total time cost of the system is minimized. This study analyzes and studies the current situation of infrastructure construction, and investment in the special town analyzes the significance and purpose of its construction and develops the research method and technical route of this study as a result. Then, the connotation of characteristic town and infrastructure is elaborated, and the value, characteristics, and role of infrastructure construction of characteristic town are analyzed; the influencing factors of infrastructure investment in characteristic town are analyzed; the development of the characteristic town has endless potential, and the development of characteristic town must meet the demand of social economy as well as town population for infrastructure support. However, there are more risk factors for infrastructure investment in characteristic towns, and the investment risk is larger than general projects, so it is necessary to strengthen risk management and do a good job of investment protection.

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Adaptive DE Algorithm for Novel Energy Control Framework Based on Edge Computing in IIoT Applications

Cited by 20 publications

References 19 publications

RETRACTED: The energy storage and optimal dispatch supply chain for new energy grids using edge computing and the internet of things

RETRACTED: The energy storage and optimal dispatch supply chain for new energy grids using edge computing and the internet of things

A Novel DBSCAN Clustering Algorithm via Edge Computing‐Based Deep Neural Network Model for Targeted Poverty Alleviation Big Data

Research on the Construction Model of 5G Special Town Based on Edge Computing

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