Real Time Energy Management and Load Forecasting in Smart Grid using CompactRIO

被引:8
|
作者
Thiyagarajan, K. [1 ]
SaravanaKumar, R. [1 ]
机构
[1] VIT Univ, Vellore 632014, Tamil Nadu, India
关键词
Load forecasting; Smart Grid; Monitoring; CompactRIO; Artificial neural networks;
D O I
10.1016/j.procs.2016.05.250
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The energy management is the process of monitoring, controlling, and conserving energy in building or organization. In this paper a real time energy management and load forecasting in smart grid based on the NI CompactRIO platform is done. A console is created to monitor the electrical load connected with the smart grid. The CompactRIO used here is to get the real time data from different electrical loads and the data is transferred and stored through console via Ethernet. Load forecasting is done by past and present data of electrical load connected with the grid using artificial neural networks. (C) 2016 The Authors. Published by Elsevier B.V.
引用
收藏
页码:656 / 661
页数:6
相关论文
共 50 条
  • [1] Real-Time Energy Management and Load Scheduling with Renewable Energy Integration in Smart Grid
    Albogamy, Fahad R.
    Khan, Sajjad Ali
    Hafeez, Ghulam
    Murawwat, Sadia
    Khan, Sheraz
    Haider, Syed Irtaza
    Basit, Abdul
    Thoben, Klaus-Dieter
    SUSTAINABILITY, 2022, 14 (03)
  • [2] Review on smart grid load forecasting for smart energy management using machine learning and deep learning techniques
    Biswal, Biswajit
    Deb, Subhasish
    Datta, Subir
    Ustun, Taha Selim
    Cali, Umit
    ENERGY REPORTS, 2024, 12 : 3654 - 3670
  • [3] Real Time Smart Grid Load Management By Integrated and Secured Communication
    Abrar, Muhammad
    Tahir, M. Ali
    Hamid, H. M. Umar
    Masroor, Roha
    PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON INNOVATIVE TRENDS IN COMPUTER ENGINEERING (ITCE' 2018), 2018, : 253 - 257
  • [4] Advanced Metering Infrastructure for Real Time Load Management in a Smart Grid
    Roy, Suryatapa
    Bedanta, Biswajeet
    Dawnee, S.
    PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON POWER AND ADVANCED CONTROL ENGINEERING (ICPACE), 2015, : 104 - 108
  • [5] Real-time load forecasting model for the smart grid using bayesian optimized CNN-BiLSTM
    Zhang, Daohua
    Jin, Xinxin
    Shi, Piao
    Chew, XinYing
    FRONTIERS IN ENERGY RESEARCH, 2023, 11
  • [6] A real-time decision model for industrial load management in a smart grid
    Yu, Mengmeng
    Lu, Renzhi
    Hong, Seung Ho
    APPLIED ENERGY, 2016, 183 : 1488 - 1497
  • [7] Real-time Control of Smart Grids using NI CompactRIO
    Bourhnane, Safae
    Abid, Mohamed Riduan
    Lghoul, Rachid
    Zine-Dine, Khalid
    Elkamoun, Najib
    Bakhouya, Mohamed
    Benhaddou, Driss
    2019 INTERNATIONAL CONFERENCE ON WIRELESS TECHNOLOGIES, EMBEDDED AND INTELLIGENT SYSTEMS (WITS), 2019,
  • [8] The Review of Demand Side Management and Load Forecasting in Smart Grid
    Zhao, Haifan
    Tang, Zhaohui
    PROCEEDINGS OF THE 2016 12TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), 2016, : 625 - 629
  • [9] An Efficient Regional Short-Term Load Forecasting Model for Smart Grid Energy Management
    Muzumdar, Ajit
    Modi, Chirag
    Vyjayanthi, C.
    IECON 2020: THE 46TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2020, : 2089 - 2094
  • [10] Forecasting Smart Grid Load on the Wire
    Xu, Jin
    Bhattacharyya, Shilpi
    Katramatos, Dimitrios
    Yoo, Shinjae
    Yue, Meng
    2018 NEW YORK SCIENTIFIC DATA SUMMIT (NYSDS), 2018,