GAN-Based Anomaly Detection and Localization of Multivariate Time Series Data for Power Plant

Choi, Young-Pil; Lim, Hee‐Jung; Choi, Heeseung; Kim, Ig-Jae

doi:10.1109/bigcomp48618.2020.00-97

Cited by 48 publications

(30 citation statements)

References 10 publications

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“…A common ground to discuss about all algorithms providing insight into the physics of the algorithms, and enabling to look for points of improvement, such as, for example, stretching the distance between the various phenomena in the high order dimensional space, may arrive from mathematical comprehension. (9) The next result obtained by this group is that, until datasets contain a versatility of tagged phenomenon, and until works of fraud detection are integrated with works about anomalies and contribution of textual customer information, then some of the works are reporting accuracy of an isolated fraud/non-fraud phenomena as conditional probability that all the rest of the phenomena are filtered out. However, when applying an algorithm in real field conditions, it may report for some of the algorithms a higher false positive ratio than expected.…”

Section: Discussionmentioning

confidence: 99%

“…where: ( )-feature and axis in high order dimensional space, which is a function of the fraud, as described by Equation (9). The meaning is for features defined in Sections 2.5-2.7 above.…”

Section: Proofmentioning

confidence: 99%

“…The work performed by Kim et al [8] is, again, investigating a dual cascaded CNN-LSTM (Long Short-Term Memory) network, and near-global weight optimization back-propagation algorithm. The work by Kim et al [9] implements the Generative Adversary Network (GAN) for anomaly detection through an auto-encoder. Korba et al [10] implemented an electricity fraud detection algorithm using "Support Vector Machine" (SVM) which requires feature generation and is used occasionally as a classifier.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Energy-Fraud Detection-System Capable of Distinguishing Frauds from Other Energy Flow Anomalies in an Urban Environment

Calamaro¹,

Beck²,

Melech³

et al. 2021

Sustainability

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Energy fraud detection bears significantly on urban ecology. Reduced losses and power consumption would affect carbon dioxide emissions and reduce thermal pollution. Fraud detection also provides another layer of urban socio-economic correlation heatmapping and improves city energy distribution. This paper describes a novel algorithm of energy fraud detection, utilizing energy and energy consumption specialized knowledge poured into AI front-end. The proposed algorithm improves fraud detection’s accuracy and reduces the false positive rate, as well as reducing the preliminary required training dataset. The paper also introduces a holistic algorithm, specifying the major phenomena that disguises as energy fraud or affects it. Consequently, a mathematical foundation for energy fraud detection for the proposed algorithm is presented. The results show that a unique pattern is obtained during fraud, which is independent of a reference non-fraud pattern of the same customer. The theory is implemented on real data taken from smart metering systems and validated in real life scenarios.

show abstract

Section: Discussionmentioning

confidence: 99%

“…where: ( )-feature and axis in high order dimensional space, which is a function of the fraud, as described by Equation (9). The meaning is for features defined in Sections 2.5-2.7 above.…”

Section: Proofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Energy-Fraud Detection-System Capable of Distinguishing Frauds from Other Energy Flow Anomalies in an Urban Environment

Calamaro¹,

Beck²,

Melech³

et al. 2021

Sustainability

View full text Add to dashboard Cite

show abstract

“…The most recent usage of images for anomaly detection was presented by Choi et al [28] that used generative adversarial networks to transform multivariate TS into images and to detect and localize anomalies in signals from power plants. Krummenacher et al [29] did an interesting work on finding anomalies within wheels of train cart wagons by transforming sensor signals into GAF and using convolutional neural networks for detecting defects.…”

Section: B Image Transformation For Anomaly Detectionmentioning

confidence: 99%

Resource-Aware Time Series Imaging Classification for Wireless Link Layer Anomalies

Bertalanič

Meža

Fortuna

2023

IEEE Trans. Neural Netw. Learning Syst.

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The number of end devices that use the last-mile wireless connectivity is dramatically increasing with the rise of smart infrastructures and requires reliable functioning to support smooth and efficient business processes. To efficiently manage such massive wireless networks, more advanced and accurate network monitoring and malfunction detection solutions are required. In this article, we perform a first-time analysis of image-based representation techniques for wireless anomaly detection using recurrence plots (RPs) and Gramian angular fields and propose a new deep learning architecture enabling accurate anomaly detection. We elaborate on the design considerations for developing a resource-aware architecture and propose a new model using time series to image transformation using RPs. We show that the proposed model: 1) outperforms the one based on Gramian angular fields by up to 14% points; 2) outperforms classical ML models using dynamic time warping by up to 24% points; 3) outperforms or performs on par with mainstream architectures, such as AlexNet and VGG11 while having <10× their weights and up to ≈8% of their computational complexity; and d) outperforms the state of the art in the respective application area by up to 55% points. Finally, we also explain on randomly chosen examples how the classifier takes decisions.

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“…In [26], authors have introduced localization framework along with transformation method for time series imaging, otherwise known a distance image. To provide practical implementation of the novel approach, an experiment is conducted on Real world smart power plant dataset which was provided by Korean thermoelectric power plant.…”

Section: Gan Applications In Energy Systemsmentioning

confidence: 99%

Synthetic Energy Data Generation Using Time Variant Generative Adversarial Network

Asre

Anwar

2022

Electronics

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Energy consumption data is being used for improving the energy efficiency and minimizing the cost. However, obtaining energy consumption data has two major challenges: (i) data collection is very expensive, time-consuming, and (ii) security and privacy concern of the users which can be revealed from the actual data. In this research, we have addressed these challenges by using generative adversarial networks for generating energy consumption profile. We have successfully generated synthetic data which is similar to the real energy consumption data. On the basis of the recent research conducted on TimeGAN, we have implemented a framework for synthetic energy consumption data generation that could be useful in research, data analysis and create business solutions. The framework is implemented using the real-world energy dataset, consisting of energy consumption data of the year 2020 for the Australian states of Victoria, New South Wales, South Australia, Queensland and Tasmania. The results of implementation is evaluated using various performance measures and the results are showcased using visualizations along with Principal Component Analysis (PCA) and t-distributed stochastic neighbor embedding (TSNE) plots. Overall, experimental results show that Synthetic data generated using the proposed implementation possess very similar characteristics to the real dataset with high comparison accuracy.

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GAN-Based Anomaly Detection and Localization of Multivariate Time Series Data for Power Plant

Cited by 48 publications

References 10 publications

An Energy-Fraud Detection-System Capable of Distinguishing Frauds from Other Energy Flow Anomalies in an Urban Environment

An Energy-Fraud Detection-System Capable of Distinguishing Frauds from Other Energy Flow Anomalies in an Urban Environment

Resource-Aware Time Series Imaging Classification for Wireless Link Layer Anomalies

Synthetic Energy Data Generation Using Time Variant Generative Adversarial Network

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