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

Hariharan Naganathan, Oswald Chong, Zigang Huang, Ying Cheng

Research output: Contribution to journalArticle

1 Citation (Scopus)

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 transform and instantaneous frequency analysis. The objectives are to develop an automated data pre-processing system that can detect anomalies and provide solutions with real-time consumption database using Ensemble Empirical Mode Decomposition(EEMD) method. The finding of this paper will also include the comparisons of Empirical mode decomposition and Ensemble empirical mode decomposition of three important type of institutional buildings.

Original languageEnglish (US)
Pages (from-to)1059-1065
Number of pages7
JournalUnknown Journal
Volume145
DOIs
StatePublished - 2016

Fingerprint

Energy utilization
Decomposition
Hot Temperature
Equipment and Supplies
Computer Systems
Real time systems
Maintenance
Databases
Dynamical systems
Temperature
Heat transfer
Sensors
Processing

Keywords

  • Anomaly Detection
  • Commercial building
  • Empirical mode decomposition
  • Hilbert Transform
  • Supply-Demand Characteristics

ASJC Scopus subject areas

  • Engineering(all)

Cite this

A Non-stationary Analysis Using Ensemble Empirical Mode Decomposition to Detect Anomalies in Building Energy Consumption. / Naganathan, Hariharan; Chong, Oswald; Huang, Zigang; Cheng, Ying.

In: Unknown Journal, Vol. 145, 2016, p. 1059-1065.

Research output: Contribution to journalArticle

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N2 - 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 transform and instantaneous frequency analysis. The objectives are to develop an automated data pre-processing system that can detect anomalies and provide solutions with real-time consumption database using Ensemble Empirical Mode Decomposition(EEMD) method. The finding of this paper will also include the comparisons of Empirical mode decomposition and Ensemble empirical mode decomposition of three important type of institutional buildings.

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