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http://dx.doi.org/10.56977/jicce.2022.20.4.295

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)
Publication Information
Journal of information and communication convergence engineering / v.20, no.4, 2022 , pp. 295-302 More about this Journal
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
Load Power; Smart Power Device; Industrial Manufacturing Machine; LSTM Model; Abnormal Data Detection;
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