Abstract

The fluctuating pattern of power plant operation can cause a negative impact on a specific area of the boiler. Such as overheating in the boiler reheater. Various methods, especially data-based modeling, have been used to optimize power plant operation to lessen the impact. Architectures such as CNN and RNN and their combinations become the main foundation in the data-based modeling process. This study is intended to reproduce previous research with the actual datasets of a boiler from a power plant in Indonesia. Because even with a similar model, datasets from different systems will produce different results. In this study, forecasting for the reheat metal temperature is carried out using logged data. The model used is a stacked-layer LSTM-based autoencoder. Various hidden unit numbers are used for each encoder-decoder to find the optimum number. The test results obtained show the accuracy of the prediction model with an average MAPE value below 0.6%. It is also concluded that the addition of hidden units has no significant impact on model performance. The model with 64-64 hidden layers is sufficient for forecasting reheat metal temperature because shorter training time with affordable performance.

Original languageEnglish
Title of host publication2022 International Seminar on Intelligent Technology and Its Applications
Subtitle of host publicationAdvanced Innovations of Electrical Systems for Humanity, ISITIA 2022 - Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages326-331
Number of pages6
ISBN (Electronic)9781665460811
DOIs
Publication statusPublished - 2022
Event23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022 - Virtual, Surabaya, Indonesia
Duration: 20 Jul 202221 Jul 2022

Publication series

Name2022 International Seminar on Intelligent Technology and Its Applications: Advanced Innovations of Electrical Systems for Humanity, ISITIA 2022 - Proceeding

Conference

Conference23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022
Country/TerritoryIndonesia
CityVirtual, Surabaya
Period20/07/2221/07/22

Keywords

  • Power plant
  • boiler
  • forecasting
  • neural network

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