Anomaly detection of building systems using energy demand frequency domain analysis

Wrinch, Michael; EL-Fouly, Tarek H. M.; Wong, Steven

doi:10.1109/pesgm.2012.6344790

Cited by 17 publications

(7 citation statements)

References 8 publications

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“…Janetzko et al [ 37 ] proposed an unsupervised anomaly detection system that used time-weighted historical power consumption data to perform predictions. Wrinch et al [ 38 ] identified anomalies by analyzing energy consumption data in a weekly moving sliding window. Hill et al [ 39 ] proposed a modeling approach diagnosing anomalies using forward predictions, without considering contexts.…”

Section: Literature Reviewmentioning

confidence: 99%

A Framework for the Automatic Integration and Diagnosis of Building Energy Consumption Data

Yuan

Lin

et al. 2021

Sensors

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Buildings account for a majority of the primary energy consumption of the human society, therefore, analyses of building energy consumption monitoring data are of significance to the discovery of anomalous energy usage patterns, saving of building utility expenditures, and contribution to the greater environmental protection effort. This paper presents a unified framework for the automatic extraction and integration of building energy consumption data from heterogeneous building management systems, along with building static data from building information models to serve analysis applications. This paper also proposes a diagnosis framework based on density-based clustering and artificial neural network regression using the integrated data to identify anomalous energy usages. The framework and the methods have been implemented and validated from data collected from a multitude of large-scale public buildings across China.

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Section: Literature Reviewmentioning

confidence: 99%

A Framework for the Automatic Integration and Diagnosis of Building Energy Consumption Data

Yuan

Lin

et al. 2021

Sensors

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“…Some frequency domain-based methods have been proposed for anomaly detection in power load curves either using Fourier Transform [50] or Wavelet Transform [51]. However, to the best of our knowledge, no solution has been proposed using Hilbert-Huang Transform.…”

Section: Detecting Abnormal Energy Demand Behaviormentioning

confidence: 99%

Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

Luque

Anguita

Pérez

et al. 2020

Sensors

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The large amount of sensors in modern electrical networks poses a serious challenge in the data processing side. For many years, spectral analysis has been one of the most used approaches to extract physically meaningful information from a sea of data. Fourier Transform (FT) and Wavelet Transform (WT) are by far the most employed tools in this analysis. In this paper we explore the alternative use of Hilbert–Huang Transform (HHT) for electricity demand spectral representation. A sequence of hourly consumptions, spanning 40 months of electrical demand in Spain, has been used as dataset. First, by Empirical Mode Decomposition (EMD), the sequence has been time-represented as an ensemble of 13 Intrinsic Mode Functions (IMFs). Later on, by applying Hilbert Transform (HT) to every IMF, an HHT spectrum has been obtained. Results show smoother spectra with more defined shapes and an excellent frequency resolution. EMD also fosters a deeper analysis of abnormal electricity demand at different timescales. Additionally, EMD permits information compression, which becomes very significant for lossless sequence representation. A 35% reduction has been obtained for the electricity demand sequence. On the negative side, HHT demands more computer resources than conventional spectral analysis techniques.

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“…This trend is present in almost every sensor trace, and, it hides the smaller fluctuations providing more specific patterns driven by local occu-pant activity. Upon deeper inspection, we uncovered several dominant patterns, common among energy-consuming devices in buildings [27]. Figure 2 depicts the auto-correlation of a usual electric power signal for a device.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…The device's consumption is decomposed using Fourier transform and outlier values are detected using clustering techniques [2,27,8]. However, these methods assume a constant periodicity in the data and this causes many false positives in alarm reporting.…”

Section: Related Workmentioning

confidence: 99%

Strip, bind, and search

Fontugne

Ortiz

Tremblay

et al. 2013

Proceedings of the 12th International Conference on Information Processing in Sensor Networks

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A typical large building contains thousands of sensors, monitoring the HVAC system, lighting, and other operational sub-systems. With the increased push for operational efficiency, operators are relying more on historical data processing to uncover opportunities for energy-savings. However, they are overwhelmed with the deluge of data and seek more efficient ways to identify potential problems. In this paper, we present a new approach called the Strip, Bind and Search (SBS); a method for uncovering abnormal equipment behavior and in-concert usage patterns. SBS uncovers relationships between devices and constructs a model for their usage pattern relative to other devices. It then flags deviations from the model. We run SBS on a set of building sensor traces; each containing hundred sensors reporting data flows over 18 weeks from two separate buildings with fundamentally different infrastructures. We demonstrate that, in many cases, SBS uncovers misbehavior corresponding to inefficient device usage that leads to energy waste. The average waste uncovered is as high as 2500 kWh per device.

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Anomaly detection of building systems using energy demand frequency domain analysis

Cited by 17 publications

References 8 publications

A Framework for the Automatic Integration and Diagnosis of Building Energy Consumption Data

A Framework for the Automatic Integration and Diagnosis of Building Energy Consumption Data

Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

Strip, bind, and search

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