Temporal Forecasting of Distributed Temperature Sensing in a Thermal Hydraulic System With Machine Learning and Statistical Models

Temporal Forecasting of Distributed Temperature Sensing in a Thermal Hydraulic System With Machine Learning and Statistical Models

We benchmark performance of long-short term memory (LSTM) network machine learning model and autoregressive integrated moving average (ARIMA) statistical model in temporal forecasting of distributed temperature sensing (DTS). Data in this study consists of fluid temperature transient measured with t...

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Bibliographic Details
Main Authors: Stella Pantopoulou, Matthew Weathered, Darius Lisowski, Lefteri H. Tsoukalas, Alexander Heifetz
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Distributed temperature sensing
Rayleigh scattering fiber optic sensors
thermal hydraulic temperature sensing
machine learning
statistical methods
ARIMA
Online Access:https://ieeexplore.ieee.org/document/10838524/
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