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A Non-stationary Analysis Using Ensemble Empirical Mode Decomposition to Detect Anomalies in Building Energy Consumption


Abstract Commercial buildings’ consumption is driven by multiple factors that include occupancy, system and equipment efficiency, thermal heat transfer, equipment plug loads, maintenance and operational procedures, and outdoor and indoor temperatures. A modern building energy system can be viewed as a complex dynamical system that is interconnected and influenced by external and internal factors. Modern large scale sensor measures some physical signals to monitor real-time system behaviors. Such data has the potentials to detect anomalies, identify consumption patterns, and analyze peak loads. The paper proposes a novel method to detect hidden anomalies in commercial building energy consumption system. The framework is based on Hilbert-Huang transfo... (more)
Created Date 2016-05-20
Contributor Naganathan, Hariharan (ASU author) / Chong, Oswald (ASU author) / Huang, Zigang (ASU author) / Cheng, Ying (ASU author) / Ira A. Fulton School of Engineering / School of Sustainable Engineering and the Built Environment / School of Electrical, Computer and Energy Engineering
Series PROCEDIA ENGINEERING
Type Text
Extent 7 pages
Language English
Identifier DOI: 10.1016/j.proeng.2016.04.137 / ISSN: 1877-7058
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Citation Naganathan, H., Chong, W. K., Huang, Z., & Cheng, Y. (2016). A Non-stationary Analysis Using Ensemble Empirical Mode Decomposition to Detect Anomalies in Building Energy Consumption. Procedia Engineering, 145, 1059-1065. doi:10.1016/j.proeng.2016.04.137
Collaborating Institutions ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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