Generation and evaluation of a synthetic dataset to improve fault detection in district heating and cooling systems

Mathieu Vallee, Thibaut Wissocq, Yacine Gaoua and Nicolas Lamaison

Energy, 2023, vol. 283, issue C

Abstract: This paper investigates various types of faults in District Heating & Cooling (DHC) systems. Many authors point out that the lack of data hinders the development of good data-driven models for fault detection and diagnosis (FDD). In this work, we design a reference dataset based on simulation and use it to evaluate Machine Learning (ML) models for fault detection.

Keywords: District heating and cooling; Synthetic dataset; Fault detection and diagnosis; Machine learning (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:283:y:2023:i:c:s0360544223017814

DOI: 10.1016/j.energy.2023.128387

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