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Evaluation of Reference Model for Thermal Energy System Based on Machine Learning Algorithm

Authors:
Minsung Kim
Young Soo Lee

Keywords: Thermal energy systems; Steady state; Machine learning; Fault detection and diagnosis.

Abstract:
Since thermal energy systems are comprised in a number of heat exchangers and fluid machinery, it is complicated and time consuming to analyze the systems mathematically. For heat pumps, a number of mathematical studies have been carried out to identify their operating status; however, accurate models are very difficult to develop due to numerous cases of different installation and operating conditions. As an alternative way to estimate the performance, a methodology using machine learning algorithm is introduced to develop a reference model. A steady-state detector with a simple low pass filter is applied to filter signals. Once steady state of the system is identified, the real-time measurements are collected to train the system model. From the study, the semi-expert based learning algorithm is effective to develop reference models of heat pump systems.

Pages: 68 to 71

Copyright: Copyright (c) IARIA, 2017

Publication date: July 23, 2017

Published in: conference

ISSN: 2308-4065

ISBN: 978-1-61208-576-0

Location: Nice, France

Dates: from July 23, 2017 to July 27, 2017