Anomaly detection for visual analytics of power consumption data

Janetzko, Halldór; Stoffel, Florian; Mittelstädt, Sebastian; Keim, Daniel A.

doi:10.1016/j.cag.2013.10.006

Cited by 94 publications

(33 citation statements)

References 25 publications

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“…However, instead of visualizing the result of data analysis, visualization is also important for improving the process of data modeling. Janetzko et al [16] discussed and presented different possibilities for visualizing the power usage time series, which enables analysts to understand the power consumption behavior and to be aware of unexpected power consumption values.…”

Section: Data Visualizationmentioning

confidence: 99%

A New Anomaly Detection System for School Electricity Consumption Data

Cui

Wang

2017

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Anomaly detection has been widely used in a variety of research and application domains, such as network intrusion detection, insurance/credit card fraud detection, health-care informatics, industrial damage detection, image processing and novel topic detection in text mining. In this paper, we focus on remote facilities management that identifies anomalous events in buildings by detecting anomalies in building electricity consumption data. We investigated five models within electricity consumption data from different schools to detect anomalies in the data. Furthermore, we proposed a hybrid model that combines polynomial regression and Gaussian distribution, which detects anomalies in the data with 0 false negative and an average precision higher than 91%. Based on the proposed model, we developed a data detection and visualization system for a facilities management company to detect and visualize anomalies in school electricity consumption data. The system is tested and evaluated by facilities managers. According to the evaluation, our system has improved the efficiency of facilities managers to identify anomalies in the data.

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Section: Data Visualizationmentioning

confidence: 99%

A New Anomaly Detection System for School Electricity Consumption Data

Cui

Wang

2017

Information

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“…Anomalies are flagged if the difference between real and predicted value is above a certain threshold. Janetzko et al [7] proposed a time-weighted prediction using historical power consumption data to identify the anomalies. Chen et al [8] transformed time series energy into symbol sequence, then clustering algorithm was used to detect outlier energy patterns.…”

Section: Power Anomaly Detection -Related Workmentioning

confidence: 99%

“…In this paper, a unique visualization technique is employed. It is built upon the work presented in [7]. The pixel-based technique is used to visually encode numerical values into colors.…”

Section: A Energy Consumption Visualizationmentioning

confidence: 99%

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Spatio - Temporal Context Anomaly Detection for Residential Power Consumption

Sisworahardjo¹,

Saad²

2017

ijeei

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Monitoring energy consumption and diagnosing abnormal behavior will enable utilities to introduce strategies to improve system resiliency, stability, and to meet energy efficiency targets. The deployment of advanced metering infrastructure (AMI) enables utilities to collect various raw data from its customers and networks. This paper presents contextual anomaly detection algorithm to detect irregular power consumption and visualize anomaly scores using unsupervised learning algorithm and temporal context generated from meter readings. The proposed algorithm computes an anomaly score for each user by considering historical consumption data. The anomaly score for a user is then adjusted by analyzing other contextual variables such as seasonal variation day of the week and other users with the same historical pattern. The implementation of realworld data set provided by power utility company shows a high performance of the proposed algorithm.

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“…4.2, the L1 distances between predict consumption and actual consumption at season s for all days observes to a log-normal distribution. Therefore, we compute Gaussian statistical model based on the L1 distance [12][13][14][15][16][17][18]. The total number of PARX models for all the time series is ||T S|| × 24, which is same as the number of the Gaussian models.…”

Section: Training Anomaly Detection Modelsmentioning

confidence: 99%

Online Anomaly Energy Consumption Detection Using Lambda Architecture

Liu

Iftikhar

Nielsen

et al. 2016

Big Data Analytics and Knowledge Discovery

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Abstract. With the widely use of smart meters in the energy sector, anomaly detection becomes a crucial mean to study the unusual consumption behaviors of customers, and to discover unexpected events of using energy promptly. Detecting consumption anomalies is, essentially, a real-time big data analytics problem, which does data mining on a large amount of parallel data streams from smart meters. In this paper, we propose a supervised learning and statistical-based anomaly detection method, and implement a Lambda system using the in-memory distributed computing framework, Spark and its extension Spark Streaming. The system supports not only iterative refreshing the detection models from scalable data sets, but also real-time anomaly detection on scalable live data streams. This paper empirically evaluates the system and the detection algorithm, and the results show the effectiveness and the scalability of the lambda detection system.

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Anomaly detection for visual analytics of power consumption data

Cited by 94 publications

References 25 publications

A New Anomaly Detection System for School Electricity Consumption Data

A New Anomaly Detection System for School Electricity Consumption Data

Spatio - Temporal Context Anomaly Detection for Residential Power Consumption

Online Anomaly Energy Consumption Detection Using Lambda Architecture

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