Beschreibung
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.
Hauptautor
Parantapa Sawant
(Institute for Sustainability in Energy and Construction (INEB), University of Applied Sciences Northwest Switzerland (FHNW))
