Comprehensive Deep Recurrent Artificial Neural Network (CDRANN): Evolutionary Model for Future Prediction

Sundar, G. Naveen; Patchaiammal, P.

doi:10.1007/978-3-030-86165-0_19

Cited by 1 publication

(1 citation statement)

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“…The gates of an LSTM cell can be summarized as follows (Rani et al, 2022;Sundar and Patchaiammal, 2022); 1. Forget Gate: It is responsible for the decision about the information that should be kept or removed from the cell state.…”

Section: Literature Surveymentioning

confidence: 99%

Teksti̇l Endüstri̇si̇nde Deri̇n Öğrenme Kullanarak Aşiri Elektri̇k Tüketi̇mi̇ni̇n Önlenmesi̇ne Yöneli̇k Bi̇r Vaka Çalişmasi

YURDOĞLU,

GÜLEÇ

2023

Mühendislik Bilimleri Ve Tasarım Dergisi

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Resources are the most critical input in the manufacturing industry therefore, resource consumption is an essential issue to be minimized. On the other hand, consumption depends on several parameters thus, it is difficult to estimate. Recently, Machine Learning (ML) and Deep Learning (DL) are powerful Artificial Intelligence (AI) subdomains for future prediction in any area. In this paper, a DL-supported electricity prediction method is designed for the textile industry as a case study in order to prevent resource over-consumption while the machines are in the standby state. This method provides dynamic consumption thresholds of electricity consumption by sliding window technique based Long-Short Term Memory (LSTM) model that helps the machines to interrupt manufacturing in their decision. These calculated thresholds are also compared with the results of Recurrent Neural Networks (RNN) and Gated Recurrent Units (GRU) as the other DL methods and Automated Regressive Integrated Moving Average (ARIMA) as a traditional method and then the results have been analyzed how close they are to real-time electricity consumption data at standby. According to the results, the LSTM model successfully predicts electricity consumption levels, sends an interrupt signal to Programmable Logic Controller (PLC) unit when the consumption levels reach the threshold and therefore prevents resource over-consumption.

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Section: Literature Surveymentioning

confidence: 99%