Design Power Controller for Smart Grid System Based on Internet of Things Devices and Artificial Neural Network

被引:2
作者
Cahyono, Muhammad Ridwan Arif [1 ]
机构
[1] Politekn Gajah Tunggal, Elect Engn, Tangerang, Indonesia
来源
2020 IEEE INTERNATIONAL CONFERENCE ON INTERNET OF THINGS AND INTELLIGENCE SYSTEM (IOTAIS) | 2021年
关键词
smart grid; Internet of Things; Artificial Neural Network; ESP32;
D O I
10.1109/IoTaIS50849.2021.9359709
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The smart grid system is an electrical infrastructure that enables consumers to sell and purchase electricity. In this paper, the smart grid model has been built with an electricity source from a solar panel connected to the load. The load has a maximum power of 40 W, and the solar panel capacity is 100 Wp. The microcontroller based on ESP32 with PZEM004t as a power sensor is used as an Internet of Things device for electric energy meter and controller for sale or purchase. As a web server, Raspberry Pi is used for smart grid data processing. This IoT system could monitor real-time load data in 24 hours. The Artificial Neural Network (ANN) with the back-propagation method was implemented in this IoT system. The ANN model has three inputs, two neuronal layers, three outputs, and four neurons per layer. A Root Mean Square Error (RMSE) of 0.12151 has been obtained from 11.000 times training process, and the test results have been achieved by RMSE of 0.10500 with an average accuracy of 89.50 percent.
引用
收藏
页码:44 / 48
页数:5
相关论文
共 24 条
  • [1] Bagus I., 2015, J LOG, V15, P63
  • [2] Baker, 2017, ICLR, P1, DOI DOI 10.48550/ARXIV.1611.02167
  • [3] Artificial Intelligence Techniques in Smart Grid and Renewable Energy Systems-Some Example Applications
    Bose, Bimal K.
    [J]. PROCEEDINGS OF THE IEEE, 2017, 105 (11) : 2262 - 2273
  • [4] Efanntyo, 2018, 2018 International Conference on Information and Communications Technology (ICOIACT), P220, DOI 10.1109/ICOIACT.2018.8350672
  • [5] Automatic selection of hidden neurons and weights in neural networks using grey wolf optimizer based on a hybrid encoding scheme
    Faris, Hossam
    Mirjalili, Seyedali
    Aljarah, Ibrahim
    [J]. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2019, 10 (10) : 2901 - 2920
  • [6] Han T, 2017, AAAI CONF ARTIF INTE, P1976
  • [7] Hodo E., 2016, 2016 International Symposium on Networks, Computers and Communications (ISNCC), P1, DOI [10.1109/ISNCC.2016.7746067, DOI 10.1109/ISNCC.2016.7746067]
  • [8] Khan S, 2018, 2018 THIRD INTERNATIONAL CONFERENCE ON FOG AND MOBILE EDGE COMPUTING (FMEC), P283, DOI 10.1109/FMEC.2018.8364080
  • [9] The internet of things: a survey
    20143600021072
    [J]. Xu, Li Da, 2015, Kluwer Academic Publishers (17)
  • [10] Intelligent Edge Computing for IoT-Based Energy Management in Smart Cities
    Liu, Yi
    Yang, Chao
    Jiang, Li
    Xie, Shengli
    Zhang, Yan
    [J]. IEEE NETWORK, 2019, 33 (02): : 111 - 117