Commercial HVAC system fault diagnosis using big data analytics: A case study

Yuhang Liu

doi:10.54254/2755-2721/13/20230722

Research Article

Open access

Published on 23 October 2023

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Liu,Y. (2023). Commercial HVAC system fault diagnosis using big data analytics: A case study. Applied and Computational Engineering,13,134-138.

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Commercial HVAC system fault diagnosis using big data analytics: A case study

Yuhang Liu *^,1,

¹ University of California-Riverside

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/13/20230722

Abstract

With the increasing use of heating, ventilating, and air conditioning (HVAC) systems nowadays, their energy consumption is receiving more attention. The study begins by trying available anomaly detection techniques, including KNN, COF, and isolated forests. The comparison reveals that these methods disregard some linear correlation results. Then, the pattern is summarized by analyzing data from 100 HVAC-equipped rooms. Next, the study uses correlation analysis and neural networks to identify abnormal HVAC data. Finally, it concludes by analyzing the factors that lead to the anomalies.

Keywords

HVAC system anomaly detection, correlation analysis, fault diagnosis, data analysis

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Cite this article

Liu,Y. (2023). Commercial HVAC system fault diagnosis using big data analytics: A case study. Applied and Computational Engineering,13,134-138.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

Conference website: https://2023.confcds.org/

ISBN：978-1-83558-017-2(Print) / 978-1-83558-018-9(Online)

Conference date: 14 July 2023

Editor：Roman Bauer, Marwan Omar, Alan Wang

Series: Applied and Computational Engineering

Volume number: Vol.13

ISSN：2755-2721(Print) / 2755-273X(Online)

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