A low power data transfer and fusion algorithm for building energy consumption monitoring

被引:2
|
作者
Li, Cuimin [1 ]
Shen, Dandan [2 ]
Wang, Lei [3 ]
机构
[1] Suzhou Univ Sci & Technol, Sch Environm Sci & Engn, Suzhou 215009, Jiangsu, Peoples R China
[2] iBest Suzhou China Low Carbon Energy Technol Co L, Suzhou 215009, Jiangsu, Peoples R China
[3] Chinese Acad Sci, Suzhou Inst Biomed Engn & Technol, Suzhou 215163, Jiangsu, Peoples R China
关键词
Building Energy Internet of Things; energy consumption monitoring; wireless sensor network; data fusion; repeatability reduction; INTERNET; SYSTEMS; THINGS;
D O I
10.1093/ijlct/ctz039
中图分类号
O414.1 [热力学];
学科分类号
摘要
Building Energy Internet of Things could collect and analyse various types of building energy consumption data in real time by means of low-energy consumption and high-precision sensing technology. In this paper, a low-energy consumption data transmission and fusion algorithm SMART-RR (Slice Mix Agg RegaTe-Repeatablibity Reduction) is proposed. Taking advantage of the periodic repeatability and data redundancy of building energy consumption data, a data fusion strategy with unequal long time intervals and adding repeatability reduction factor is proposed. The simulation results show that SMART-RR algorithm is a low-energy data transmission and fusion algorithm with small data traffic, high privacy protection and high accuracy.
引用
收藏
页码:426 / 431
页数:6
相关论文
共 50 条
  • [41] Reconstructing building stock to replicate energy consumption data
    Zhao, Fei
    Lee, Sang Hoon
    Augenbroe, Godfried
    ENERGY AND BUILDINGS, 2016, 117 : 301 - 312
  • [42] Development of Energy Consumption Monitoring Platform for Managing Public Building
    Yao, Kuanyi
    Zhang, Li
    ADVANCES IN CIVIL ENGINEERING AND ARCHITECTURE INNOVATION, PTS 1-6, 2012, 368-373 : 3509 - 3512
  • [43] A relevant data selection method for energy consumption prediction of low energy building based on support vector machine
    Paudel, Subodh
    Elmitri, Mohamed
    Couturier, Stephane
    Nguyen, Phuong H.
    Kamphuis, Rene
    Lacarriere, Bruno
    Le Corre, Olivier
    ENERGY AND BUILDINGS, 2017, 138 : 240 - 256
  • [44] The typical application of building energy consumption monitoring and energy management: A case study
    Zhao, Liang
    Liang, Ruobing
    Zhang, Jili
    International Journal of Smart Home, 2015, 9 (01): : 177 - 188
  • [45] Adaptive-speed/CAV algorithm in a CD-ROM drive to accomplish high data transfer rate and low power consumption
    Stan, S.G.
    Van Kempen, H.
    Lin, Ch.-Ch.S.
    Yen, M.-S.M.
    Wang, W.W.
    IEEE Transactions on Magnetics, 1998, 34 (2 pt 1): : 407 - 410
  • [46] Adaptive-speed/CAV algorithm in a CD-ROM drive to accomplish high data transfer rate and low power consumption
    Stan, SG
    van Kempen, H
    Lin, CCS
    Yen, MSM
    Wang, WW
    IEEE TRANSACTIONS ON MAGNETICS, 1998, 34 (02) : 407 - 410
  • [47] Building lighting energy consumption prediction for supporting energy data analytics
    Amasyali, Kadir
    El-Gohary, Nora
    ICSDEC 2016 - INTEGRATING DATA SCIENCE, CONSTRUCTION AND SUSTAINABILITY, 2016, 145 : 511 - 517
  • [48] An outlier management framework for building performance data and its application to the power consumption data of building energy systems in non-residential buildings
    Zhao, Tianyi
    Sun, Yue
    Chai, Zhuyue
    Li, Kuishan
    JOURNAL OF BUILDING ENGINEERING, 2023, 65
  • [49] Prediction and Management of Building Energy Consumption Based on Building Environment Simulation Design Platform DeST and Meteorological Data Analysis Algorithm
    Bai, Chaoqin
    Liu, Junrui
    Strategic Planning for Energy and the Environment, 2024, 43 (02) : 357 - 380
  • [50] A Low-Power-Consumption Boost Converter with Maximum Power Tracking Algorithm for Indoor Photovoltaic Energy Harvesting
    Tsai, Dian-Lin
    Wu, Hung-Hsien
    Wei, Chia-Ling
    2017 IEEE WIRELESS POWER TRANSFER CONFERENCE (WPTC 2017), 2017,