Point and contextual anomaly detection in building load profiles of a university campus

Wang, Long; Turowski, Marian; Zhang, Meng; Riedel, Till; Beigl, Michael; Mikut, Ralf; Hagenmeyer, Veit

doi:10.1109/isgt-europe47291.2020.9248792

Cited by 10 publications

(3 citation statements)

References 13 publications

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“…Also, only one detecting approach was studied. Wang et al applied and compared four algorithms, Deep Neural Network Regression (DNNR), Autoencoder with reconstruction (AER), encoder of the Autoencoder (EAE), and Support Vector Regression (SVR), in a task of detecting electricity meter failure (point anomalies) and unusual electricity consumption (contextual anomalies) [43]. The unsupervised AER was the optimal model for detecting point anomalies, and the unsupervised EAE performed almost equally well as the optimal model DNNR in detecting contextual anomalies.…”

Section: Related Workmentioning

confidence: 99%

Toward Delicate Anomaly Detection of Energy Consumption for Buildings: Enhance the Performance From Two Levels

Wang

Enlund²,

Trygg

et al. 2022

IEEE Access

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Buildings are highly energy-consuming and therefore are largely accountable for environmental degradation. Detecting anomalous energy consumption is one of the effective ways to reduce energy consumption. Besides, it can contribute to the safety and robustness of building systems since anomalies in the energy data are usually the reflection of malfunctions in building systems. As the most flexible and applicable type of anomaly detection approach, unsupervised anomaly detection has been implemented in several studies for building energy data. However, no studies have investigated the joint influence of data structures and algorithms' mechanisms on the performance of unsupervised anomaly detection for building energy data. Thus, we put forward a novel workflow based on two levels, data structure level and algorithm mechanism level, to effectively detect the imperceptible anomalies in the energy consumption profiles of buildings. The proposed workflow was implemented in a case study for identifying the anomalies in three real-world energy consumption datasets from two types of commercial buildings. Two aims were achieved through the case study. First, it precisely detected the contextual anomalies concealed beneath the time variation of the energy consumption profiles of the three buildings. The performance in terms of areas under the precision-recall curves (AUC_PR) for the three given datasets were 0.989, 0.941, and 0.957, respectively. Second, more broadly, the joint effect of the two levels was examined. On the data level, all four detectors on the contextualized data were superior to their counterparts on the original data. On the algorithm level, there was a consistent ranking of detectors regarding their detecting performances on the contextualized data. The consistent ranking suggests that local approaches outperform global approaches in the scenarios where the goal is to detect the instances deviating from their contextual neighbors rather than the rest of the entire data.

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Section: Related Workmentioning

confidence: 99%

Toward Delicate Anomaly Detection of Energy Consumption for Buildings: Enhance the Performance From Two Levels

Wang

Enlund²,

Trygg

et al. 2022

IEEE Access

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“…However, these recorded time series mostly contain anomalies [38]. Anomalies are patterns that deviate from "a well defined notion of normal behavior" [5, p. 15:2] and can arise for many causes such as atypical user behavior [25], smart meter failures [37], and energy theft [40]. These deviations can result in data points or patterns in the time series that represent wrong or misleading information and may be especially problematic for down-stream applications [38].…”

Section: Introductionmentioning

confidence: 99%

Enhancing anomaly detection methods for energy time series using latent space data representations

Turowski

Heidrich

Phipps

et al. 2022

Proceedings of the Thirteenth ACM International Conference on Future Energy Systems

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“…The accuracy of data acquisition refers to the correctness of the recorded data. It is reduced by problems causing, for example, noise and outliers in the data [7], [8]. For further processing, outliers in particular are often detected and labeled as missing values as a first step [2], [9].…”

Section: Introduction and State Of The Artmentioning

confidence: 99%

Data-Driven Copy-Paste Imputation for Energy Time Series

Weber

Turowski

Çakmak

et al. 2021

IEEE Trans. Smart Grid

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A cornerstone of the worldwide transition to smart grids are smart meters. Smart meters typically collect and provide energy time series that are vital for various applications, such as grid simulations, fault-detection, load forecasting, load analysis, and load management. Unfortunately, these time series are often characterized by missing values that must be handled before the data can be used. A common approach to handle missing values in time series is imputation. However, existing imputation methods are designed for power time series and do not take into account the total energy of gaps, resulting in jumps or constant shifts when imputing energy time series. In order to overcome these issues, the present paper introduces the new Copy-Paste Imputation (CPI) method for energy time series. The CPI method copies data blocks with similar characteristics and pastes them into gaps of the time series while preserving the total energy of each gap. The new method is evaluated on a real-world dataset that contains six shares of artificially inserted missing values between 1 and 30%. It outperforms the three benchmark imputation methods selected for comparison. The comparison furthermore shows that the CPI method uses matching patterns and preserves the total energy of each gap while requiring only a moderate run-time.

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Point and contextual anomaly detection in building load profiles of a university campus

Cited by 10 publications

References 13 publications

Toward Delicate Anomaly Detection of Energy Consumption for Buildings: Enhance the Performance From Two Levels

Toward Delicate Anomaly Detection of Energy Consumption for Buildings: Enhance the Performance From Two Levels

Enhancing anomaly detection methods for energy time series using latent space data representations

Data-Driven Copy-Paste Imputation for Energy Time Series

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