A commissioning-oriented fault detection framework for building heating systems using SARIMAX models
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Publication date
29.10.2024
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04B - Conference paper
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Proceedings of the 2024 Upper-Rhine Artificial Intelligence Symposium
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Offenburg
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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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Upper-Rhine Artificial Intelligence Symposium 2024
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13.11.2024
Conference end date
14.11.2024
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English
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Yes
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Zero Emission
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Published
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Diamond
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Citation
Sawant, P., & Eismann, R. (2024). A commissioning-oriented fault detection framework for building heating systems using SARIMAX models. In Hochschule Offenburg (Ed.), Proceedings of the 2024 Upper-Rhine Artificial Intelligence Symposium. https://doi.org/10.26041/fhnw-10803