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

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.The dataset itself covers six types of DHC system components, covering production, distribution and storage. It is provided as Open Data with corresponding documentation. Most of the models used for generating the dataset are provided as Open Source code.To assess the usefulness of the dataset, we evaluated five ML models on five fault detection tasks. The results highlight varying level of performance on the considered tasks, with faults related to global energy efficiency being easier to handle than those related specifically to thermal losses. Three of the investigated models (Logistic Regression, Support Vector Machine, and XGBoost) provide consistent performance on the considered tasks, achieving accuracy scores up to 99% on the easier tasks and above 66% on one of the more difficult tasks. We also illustrate possibilities for transferring models to real systems with different characteristics, with encouraging results.