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

2021 ◽  
Author(s):  
Muhammad Ridwan Arif Cahyono
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internet Of Things ◽  
Smart Grid ◽  
Grid System ◽  
Power Controller ◽  
Artificial Neural ◽  
Smart Grid System ◽  
Design Power

scholarly journals Desain Sistem Informasi Cerdas pada Smart Grid Berbasis Internet of Things dan Artificial Neural Network

2021 ◽  
Vol 4 (1) ◽  
pp. 11-19
Author(s):  
Muhammad Ridwan Arif Cahyono ◽  
Surya Wirawan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internet Of Things ◽  
Smart Grid ◽  
Black Box ◽  
Raspberry Pi ◽  
Mean Square ◽  
Layer Neuron ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Smart grid merupakan sistem kelistrikan yang memungkinkan pengguna untuk melakukan proses menjual dan membeli daya listrik. Pada penelitian ini dirancang model smart grid dengan sumber daya dari listrik PLN dan panel surya yang terhubung dengan beban. Beban yang digunakan memiliki daya maksimal 40 W dan panel surya yang digunakan memiliki kapasitas 100 Wp. ESP32 digunakan sebagai perangkat Internet of Things, yang digunakan sebagai pengukur dan pengontrol daya listrik yang akan dijual atau dibeli. Raspberry Pi digunakan sebagai web server pengolah data dari smart grid. Aplikasi “Smart Grid Dikti” merupakan aplikasi berbasis android yang dapat digunakan untuk melakukan pemantauan serta pengaturan dalam sistem smart grid tersebut. Aplikasi android tersebut telah diuji coba dengan metode Black Box, dengan hasil pengujian 100% berhasil. Kecerdasan buatan berbasis Artificial Neural Network (ANN) dengan metode backpropagation diimplementasikan dalam sistem smart grid yang berfungsi sebagai pengaturan otomatis dalam proses jual dan beli daya listrik. ANN yang digunakan memiliki 3 input, 2 layer neuron, 3 output, dan masing-masing layer memiliki 4 neuron yang diimplementasikan ke dalam bahasa Python. Setelah pelatihan sebanyak 11.000 kali, didapatkan Root Mean Square  Error (RMSE) sebesar 0,12151 dan pada saat uji coba didapatkan RMSE sebesar 0,10500 dengan akurasi rata-rata sebesar 89,50%.

scholarly journals Optimization of Load Forecasting in Smartgrid using Artificial Neural Network based NFTOOL and NNTOOL

2022 ◽  
Vol 2161 (1) ◽  
pp. 012068
Author(s):  
Sthitprajna Mishra ◽  
Bibhu Prasad Ganthia ◽  
Abel Sridharan ◽  
P Rajakumar ◽  
D. Padmapriya ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Smart Grid ◽  
User Interfaces ◽  
Load Forecasting ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Load Prediction ◽  
Artificial Neural ◽  
Optimal Reserve

Abstract The motivation behind the research is the requirement of error-free load prediction for the power industries in India to assist the planners for making important decisions on unit commitments, energy trading, system security & reliability and optimal reserve capacity. The objective is to produce a desktop version of personal computer based complete expert system which can be used to forecast the future load of a smart grid. Using MATLAB, we can provide adequate user interfaces in graphical user interfaces. This paper devotes study of load forecasting in smart grid, detailed study of architecture and configuration of Artificial Neural Network(ANN), Mathematical modeling and implementation of ANN using MATLAB and Detailed study of load forecasting using back propagation algorithm.

scholarly journals Security-Aware Routing Protocol Based on Artificial Neural Network Algorithm and 6LoWPAN in the Internet of Things

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Jiangdong Lu ◽  
Dongfang Li ◽  
Penglong Wang ◽  
Fen Zheng ◽  
Meng Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internet Of Things ◽  
Routing Protocols ◽  
Intelligent Systems ◽  
Smart Devices ◽  
The Internet ◽  
Routing Mechanism ◽  
Artificial Neural ◽  
The Internet Of Things

Today, with increasing information technology such as the Internet of Things (IoT) in human life, interconnection and routing protocols need to find optimal solution for safe data transformation with various smart devices. Therefore, it is necessary to provide an enhanced solution to address routing issues with respect to new interconnection methodologies such as the 6LoWPAN protocol. The artificial neural network (ANN) is based on the structure of intelligent systems as a branch of machine interference, has shown magnificent results in previous studies to optimize security-aware routing protocols. In addition, IoT devices generate large amounts of data with variety and accuracy. Therefore, higher performance and better data handling can be achieved when this technology incorporates data for sending and receiving nodes in the environment. Therefore, this study presents a security-aware routing mechanism for IoT technologies. In addition, a comparative analysis of the relationship between previous approaches discusses with quality of service (QoS) factors such as throughput and accuracy for improving routing mechanism. Experimental results show that the use of time-division multiple access (TDMA) method to schedule the sending and receiving of data and the use of the 6LoWPAN protocol when routing the sending and receiving of data can carry out attacks with high accuracy.

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