Work

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

Year: 2024

Type: article

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.

Source: OJS

DOI: 10.60643/urai.v2024p59

Authors: Sawant, Parantapa, Eismann, Ralph

Institution: FHNW University of Applied Sciences and Arts Northwestern Switzerland

Language: English

Topic: Fault Detection and Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W6976408359

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