Abstract
The smart heating, ventilation and air conditioning systems (HVAC) are able to reduce energy consumption by fitting it better to the users’ behavior or building and equipment needs. Monitoring the HVAC state allows detecting the anomalous deviations in the system. The paper presents the visualization-driven approach to the analysis of the HVAC systems logs that can be applied for monitoring in the real time mode. The authors propose to define the time periods during which the state of the system remains almost unchanged and visualize the average values of the HVAC parameters using the heat map. This allows attracting the analyst attention to the changes in the HVAC system as well as detecting its life rhythm. We demonstrate our approach with an application to the VAST MiniChallenge-2 2016 data set, which describes the functioning of the three-storey building.
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References
Jones, M.: Smart heating and ventilation systems at high risk of attack. https://techhq.com/2019/01/smart-heating-and-ventilation-systems-at-high-risk-of-attack/. Citied 07 March 2019
Vast Challenge Homepage. http://vacommunity.org/. Citied 10 Apr 2019
Munir, M., Erkel, S.,Dengel, A., Ahmed, A.: Pattern-based contextual anomaly detection in HVAC systems. In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), New Orleans, LA (2017). https://doi.org/10.1109/ICDMW.2017.150
Cui, W., Wang, H.: Anomaly detection and visualization of school electricity consumption data. In: 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), Beijing (2017). https://doi.org/10.1109/ICBDA.2017.8078707
Satta, R., Cavallari, S., Pomponi, E., Grasselli, D., Picheo, D., Annis, A.: A dissimilarity-based approach to predictive maintenance with application to HVAC systems. https://arxiv.org/abs/1701.03633. Citied 07 March 2019
Truong, H., Francisco, A., Khosrowpour, A., Taylor, J.E., Mohammadi, N.: Method for visualizing energy use in building information models. Energy Procedia (2017). https://doi.org/10.1016/j.egypro.2017.12.089
Palm, J., EllegĂĄrd, K.: Visualizing energy consumption activities as a tool for developing effective policy. Int. J. Consumer Stud. (2011). https://doi.org/10.1111/j.1470-6431.2010.00974.x
Abdelalim, A., O’Brien, W., Shi., Z.: Development of sankey diagrams to visualize real HVAC performance. Energy Buildings (2017). https://doi.org/10.1016/j.enbuild.2017.05.040.
Janetzko, H., Stoffel, F., Mittelstädt, A., Keim, D.A.: Anomaly detection for visual analytics of power consumption data. Comput. Graph. (2014). https://doi.org/10.1016/j.cag.2013.10.006
Fischer F., Keim D. A.: NstreamAware: real-time visual analytics for data streams to enhance situational awareness. In: Proceedings of the Eleventh Workshop on Visualization for Cyber Security (VizSec 2014). ACM (2014). https://doi.org/10.1145/2671491.2671495
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis, E., Han, J., Fayyad, U. (eds.) Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD 1996). AAAI Press, pp. 226-231 (1996)
Cappers, B.C.M., van Wijk, J.: SNAPS: semantic network traffic analysis through projection and selection. In: Proceedings of the IEEE Symposium on Visualization for Cyber Security (2015). https://doi.org/10.1109/VIZSEC.2015.7312768
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Novikova, E., Bestuzhev, M., Shorov, A. (2020). The Visualization-Driven Approach to the Analysis of the HVAC Data. In: Kotenko, I., Badica, C., Desnitsky, V., El Baz, D., Ivanovic, M. (eds) Intelligent Distributed Computing XIII. IDC 2019. Studies in Computational Intelligence, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-030-32258-8_64
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DOI: https://doi.org/10.1007/978-3-030-32258-8_64
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