Predicting Steam Turbine Power Generation: A Comparison of Long Short-Term Memory and Willans Line Model

Mostafa Pasandideh (), Matthew Taylor, Shafiqur Rahman Tito, Martin Atkins and Mark Apperley
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Mostafa Pasandideh: Ahuora—Centre for Smart Energy Systems, School of Computing and Mathematical Sciences, University of Waikato, Hamilton 3240, New Zealand
Matthew Taylor: Ahuora—Centre for Smart Energy Systems, School of Engineering, University of Waikato, Hamilton 3240, New Zealand
Shafiqur Rahman Tito: Ahuora—Centre for Smart Energy Systems, School of Computing and Mathematical Sciences, University of Waikato, Hamilton 3240, New Zealand
Martin Atkins: Ahuora—Centre for Smart Energy Systems, School of Engineering, University of Waikato, Hamilton 3240, New Zealand
Mark Apperley: Ahuora—Centre for Smart Energy Systems, School of Computing and Mathematical Sciences, University of Waikato, Hamilton 3240, New Zealand

Energies, 2024, vol. 17, issue 2, 1-15

Abstract: This study focuses on using machine learning techniques to accurately predict the generated power in a two-stage back-pressure steam turbine used in the paper production industry. In order to accurately predict power production by a steam turbine, it is crucial to consider the time dependence of the input data. For this purpose, the long-short-term memory (LSTM) approach is employed. Correlation analysis is performed to select parameters with a correlation coefficient greater than 0.8. Initially, nine inputs are considered, and the study showcases the superior performance of the LSTM method, with an accuracy rate of 0.47. Further refinement is conducted by reducing the inputs to four based on correlation analysis, resulting in an improved accuracy rate of 0.39. The comparison between the LSTM method and the Willans line model evaluates the efficacy of the former in predicting production power. The root mean square error (RMSE) evaluation parameter is used to assess the accuracy of the prediction algorithm used for the generator’s production power. By highlighting the importance of selecting appropriate machine learning techniques, high-quality input data, and utilising correlation analysis for input refinement, this work demonstrates a valuable approach to accurately estimating and predicting power production in the energy industry.

Keywords: machine learning techniques; prediction/forecasting; long short-term memory (LSTM); Willans line model; generator’s power production (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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