Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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LSTM Model-based Prediction of the Variations in Load Power Data from Industrial Manufacturing Machines

  • Rita, Rijayanti (Department of Information and Communication Engineering, Changwon National University) ;
  • Kyohong, Jin (Department of Electronic Engineering, Changwon National University) ;
  • Mintae, Hwang (Department of Information and Communication Engineering, Changwon National University)
  • Received : 2022.10.12
  • Accepted : 2022.12.11
  • Published : 2022.12.31
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Abstract

This paper contains the development of a smart power device designed to collect load power data from industrial manufacturing machines, predict future variations in load power data, and detect abnormal data in advance by applying a machine learning-based prediction algorithm. The proposed load power data prediction model is implemented using a Long Short-Term Memory (LSTM) algorithm with high accuracy and relatively low complexity. The Flask and REST API are used to provide prediction results to users in a graphical interface. In addition, we present the results of experiments conducted to evaluate the performance of the proposed approach, which show that our model exhibited the highest accuracy compared with Multilayer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM) models. Moreover, we expect our method's accuracy could be improved by further optimizing the hyperparameter values and training the model for a longer period of time using a larger amount of data.

Keywords

Acknowledgement

This work was supported by Gyeongnam SW Convergence Cluster 2.0 under the contract and the "Leaders in INdustry-university Cooperation +" Project by the Ministry of Education and National Research Foundation of Korea.

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