Generation and evaluation of a synthetic dataset to improve fault detection in district heating and cooling systems
Résumé
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). This work aims at providing the research community with a reference dataset of simulated faults for DHC components, as well as demonstrating the usefulness of the approach.
The dataset itself covers six types of DHC system components, covering production, distribution and storage. It is provided as Open Data with corresponding documentation. The models used for generating the dataset are mostly based on Open Source Modelica libraries, and are provided as Open Source code.
To assess the usefulness of the dataset, we provide evaluation of five Machine Learning (ML) models. The results highlight discrepancies among the considered tasks, with faults related to global energy efficiency being easier to handle than those related specifically to thermal losses. We also observe that three of the investigated models (Logistic Regression, Support Vector Machine, and XGBoost) provide consistent performance on the considered tasks. The key novelty of this paper is to present, document and evaluate a consistent and reusable framework for boosting research on FDD in DHC systems.
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