The Visualization-Driven Approach to the Analysis of the HVAC Data

Novikova, Evgenia; Bestuzhev, Mikhail; Shorov, Andrey

doi:10.1007/978-3-030-32258-8_64

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…They also highlighted the crucial role of human involvement in refining fault detection in contemporary buildings. In another study, Novikova et al [34] introduced a visualization-centric approach for real-time HVAC system log analysis, focusing on the clustering of stable operational patterns. By utilizing a modified density-based clustering technique, they expertly condensed vast BAS data of a three-story building into discernible patterns, represented via heat maps.…”

Section: Unsupervised Learning Methodsmentioning

confidence: 99%

Machine learning-based fault detection and preliminary diagnosis for terminal air-handling units

Rajabi¹,

Mokhtari²,

McArthur³

2023

Computing in Construction

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The rise of AI-powered classification techniques has ushered in a new era for data-driven Fault Detection and Diagnosis (FDD) in smart building systems. While extensive research has championed supervised FDD approaches, the real-world application of unsupervised methods remains limited. Among these, cluster analysis stands out for its potential with Building Management System (BMS) data. This study introduces an unsupervised learning strategy to detect faults in terminal air handling units and their associated systems. The methodology involves pre-processing historical sensor data using Principal Component Analysis (PCA) to streamline dimensions. This is then followed by OPTICS clustering, juxtaposed against kmeans for comparison. The effectiveness of the proposed strategy was gauged using several labeled datasets depicting various fault scenarios and real-world building BMS data. Results showed that OPTICS consistently surpassed k-means in accuracy across seasons. Notably, OPTICS offers a unique visualization feature for users called "reachability distance," allowing a preview of detected clusters before setting thresholds. Moreover, according to the results, while PCA is beneficial for reducing computational costs and enhancing noise reduction-thereby generally improving the clarity of cluster differentiation in reachability distance-it also has its limitations, particularly in complex fault scenarios. In such cases, PCA's dimensionality reduction may result in the loss of critical information, leading to some clusters being less discernible or entirely undetected. These overlooked clusters could be indicative of underlying faults, and their obscurity represents a significant limitation of PCA when identifying potential fault lines in intricate datasets.

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Section: Unsupervised Learning Methodsmentioning

confidence: 99%

Machine learning-based fault detection and preliminary diagnosis for terminal air-handling units

Rajabi¹,

Mokhtari²,

McArthur³

2023

Computing in Construction

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“…In the approach the density-based clustering DBSCAN algorithm is used to construct data slices [9]. As it is initially designed to analyze static data without temporal attributes it was modified to consider the temporal order of data points [7].…”

Section: Fig 1 Radviz Presentation Of the Hvac Data For 8 Working Daysmentioning

confidence: 99%

“…The ease of comprehension of information displayed in the visualization by the analyst defines the efficiency of applying visual analytics techniques for pattern and anomaly detection in HVAC data. However, in many cases the existing visual analytics approaches proposed to analyze the HVAC or energy consumption data [3][4][5][6][7] did not undergo any usability assessment focusing both on their efficiency in solving tasks and easiness of their comprehension.…”

Section: Introductionmentioning

confidence: 99%

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Usability Assessment of the Visualization-Driven Approaches to the HVAC Data Exploration

Novikova

Belimova

Dzhumagulova

et al. 2020

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2

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Application of the Internet-connected operational devices in the heating, ventilation and conditioning (HVAC) systems has extended the cyber-attack surface by introducing different malicious scenarios. The analysis of the HVAC data may provide insight on typical patterns of the system operations. Implementation of the thoroughly elaborated visualization models may significantly increase the efficiency of the suspicious activity identification in the HVAC systems. In the paper we present the results of the laboratory usability testing of three visualization models used to analyze HVAC data – matrix-based visualization technique, non-linear multidimensional visualization technique RadViz and timeline chart. Matrix-based visualization and RadViz visualization are often used in anomaly detection process, while timeline charts are a traditional way to present operational HVAC data. We describe the experiment design and discuss the results obtained. The usability testing revealed advantages and limitations of these visualization techniques in behavior pattern and anomaly identification tasks. The results can further serve as guidelines for task-dependent selection of a visualization technique.

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