A distributed energy monitoring network system based on data fusion via improved PSO

被引:12
|
作者
Sung, Wen-Tsai [1 ]
Chung, Hung-Yuan [2 ]
机构
[1] Natl Chin Yi Univ Technol, Dept Elect Engn, Taichung 41170, Taiwan
[2] Natl Cent Univ, Dept Elect Engn, Jhongli, Taoyuan County, Taiwan
关键词
Particle Swarm Optimization; Wireless sensor networks; Embedded systems; Distributed energy monitoring; PARTICLE SWARM OPTIMIZATION; SENSORS; DESIGN;
D O I
10.1016/j.measurement.2014.05.007
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This study uses ZigBee wireless sensor network technology to build a distributed energy monitoring and management research system. A network of nodes can immediately capture the energy of the state in order to collect and analyze information through ZigBee wireless devices transmitting to a central system network server host. With an improved Particle Swarm Optimization (IPSO) approach to data integration optimization calculations, the results of this study will allow the construction of a distributed energy network monitoring system to obtain the optimal solution. The results developed in this study can be applied to energy management, environmental management, information management, plant monitoring, renewable energy management and other fields. In addition to emphasizing the GIO embedded system design techniques for automating energy management, this paper also notes the importance of building distributed nodes using ZigBee technology and energy management system control for addressing such situations as factory fires, theft and energy management security monitoring systems development. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:362 / 374
页数:13
相关论文
共 50 条
  • [1] EV Energy Storage Monitoring System Based on Distributed Data Acquisition
    Lu Ren-gui
    Pei Lei
    Ma Rui
    Wei Jun-lei
    Zhu Chun-bo
    [J]. 2009 IEEE VEHICLE POWER AND PROPULSION CONFERENCE, VOLS 1-3, 2009, : 1186 - 1189
  • [2] Multisensor data fusion of motion monitoring system based on BP neural network
    Shuxin Wang
    [J]. The Journal of Supercomputing, 2020, 76 : 1642 - 1656
  • [3] Multisensor data fusion of motion monitoring system based on BP neural network
    Wang, Shuxin
    [J]. JOURNAL OF SUPERCOMPUTING, 2020, 76 (03): : 1642 - 1656
  • [4] Improved PSO based clustering fusion algorithm for multimedia image data projection
    Feng Pan
    Deqiang Chen
    Lu Lu
    [J]. Multimedia Tools and Applications, 2020, 79 : 9509 - 9522
  • [5] Improved PSO based clustering fusion algorithm for multimedia image data projection
    Pan, Feng
    Chen, Deqiang
    Lu, Lu
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2020, 79 (13-14) : 9509 - 9522
  • [6] An Improved Distributed Data Fusion Method
    Tian, Lai
    Pan, Xiang
    Wu, Zhaolin
    Liu, Jinfeng
    [J]. 2018 IEEE 4TH INTERNATIONAL CONFERENCE ON CONTROL SCIENCE AND SYSTEMS ENGINEERING (ICCSSE 2018), 2018, : 484 - 488
  • [7] Modified covariance intersection for data fusion in distributed nonhomogeneous monitoring systems network
    Daeichian, Abolghasem
    Honarvar, Elham
    [J]. INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2018, 28 (04) : 1413 - 1424
  • [8] RT Component Based Network Distributed Monitoring System
    Jia, Songmin
    Takase, Kunikatsu
    [J]. JOURNAL OF ROBOTICS AND MECHATRONICS, 2008, 20 (01) : 82 - 88
  • [9] Research on Building Energy Consumption Prediction Based on Improved PSO Fusion LSSVM Model
    Zhang, Suli
    Chang, Yiting
    Li, Hui
    You, Guanghao
    [J]. ENERGIES, 2024, 17 (17)
  • [10] Managing a distributed data fusion network
    Nicholson, D
    Leung, V
    [J]. SIGNAL PROCESSING, SENSOR FUSION, AND TARGET RECOGNITION XIII, 2004, 5429 : 129 - 137