Commissioning-Oriented Fault Detection Framework for Building Heating Systems Using SARIMAX Models

2025/01/07 by Sawant, Parantapa, Eismann, Ralph
#Building Technologies #Data-Driven Fault Detection #SARIMAX

paper · doi:10.60643/urai.v2024p59

Abstract

A scalable and rapidly deployable fault detection framework for building heating systems is presented. Unlike existing data-intensive machine learning approaches, a SARIMAX-based concept was implemented to address challenges with limited data availability after commissioning of the plant. The effectiveness of this framework is demonstrated on real-world data from multiple solar thermal systems, indicating potential for extensive field tests and applications for broader systems, including heat pumps and district heating.

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