Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/179791
Title: Transfer learning mechanism to account performance degradation in gas turbines with limited operational data
Authors: Thangavelu, Sundar Raj
Gunasekera, Imantha
Tafone, Alessio
Shigenori, Morita
Keywords: Engineering
Issue Date: 2023
Source: Thangavelu, S. R., Gunasekera, I., Tafone, A. & Shigenori, M. (2023). Transfer learning mechanism to account performance degradation in gas turbines with limited operational data. 36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2023), 3275-3283. https://dx.doi.org/10.52202/069564-0294
Project: IAF-ICP I1801E0020 
Conference: 36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2023)
Abstract: This paper investigates the transfer learning mechanism to improve the prediction accuracy of the energy system model. Artificial intelligence techniques are increasingly being adopted in the energy domain to predict energy system characteristics and performance. However, in many energy systems, the relationship between interested variables and their distributions differs (data and concept drift) with time due to system degradation and aging. There is a requirement for re-training and re-testing AI models to ensure reliable performance over time, which may require extensive latest operational data. Transfer learning helps to confront this challenge by leveraging valuable knowledge from a pre-trained model and reducing the requirement of new operational data significantly. To address these issues, this paper focuses on a gas turbine, a critical energy system widely deployed in diverse applications, and shows performance degradation over its lifetime. The energy model of the gas turbine is a multivariate type that predicts energy efficiency, fuel consumption, and heat energy based on power setpoints and weather conditions. This paper examined transfer learning mechanisms that can capture the latest characteristics of the gas turbine and their effects on prediction accuracy. The developed transfer learning model predicts fuel consumption accurately above 99%, whereas the pre-trained model under-predicts up to 4%, which may lead to suboptimal operation decisions when employed in the scheduling algorithm. Some of the other targets, such as heat energy, show marginal drift, as expected from the gas turbine characteristics. The knowledge gained from the transfer learning mechanism and its efficacy boost assists operational decisions, which helps improve energy efficiency and cost savings.
URI: https://hdl.handle.net/10356/179791
URL: https://www.proceedings.com/69563.html
https://www.proceedings.com/069564-0294.html
ISBN: 9781713874928
DOI: 10.52202/069564-0294
Schools: School of Mechanical and Aerospace Engineering 
Research Centres: Energy Research Institute @ NTU (ERI@N) 
Rights: © 2023 ECOS. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at http://doi.org/10.52202/069564-0294.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ERI@N Conference Papers

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