Big Data Platform for Smart Grids Power Consumption Anomaly Detection

Lipcak, Peter; Macák, Martin; Rossi, Bruno

doi:10.15439/2019f210

Cited by 12 publications

(5 citation statements)

References 34 publications

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“…The earlier work did not focus on evaluation [17], and this work may be considered as one step toward evaluating the RA. A similar mapping between the earlier version of our RA [52] and an implementation architecture has been performed for a system focusing on anomaly detection on power consumption [53]. Their work covered all functional areas of the RA [52], which was not covered by this work for the newer version of the RA [17].…”

Section: Discussionmentioning

confidence: 99%

Architecture for Enabling Edge Inference via Model Transfer from Cloud Domain in a Kubernetes Environment

et al. 2020

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The current approaches for energy consumption optimisation in buildings are mainly reactive or focus on scheduling of daily/weekly operation modes in heating. Machine Learning (ML)-based advanced control methods have been demonstrated to improve energy efficiency when compared to these traditional methods. However, placing of ML-based models close to the buildings is not straightforward. Firstly, edge-devices typically have lower capabilities in terms of processing power, memory, and storage, which may limit execution of ML-based inference at the edge. Secondly, associated building information should be kept private. Thirdly, network access may be limited for serving a large number of edge devices. The contribution of this paper is an architecture, which enables training of ML-based models for energy consumption prediction in private cloud domain, and transfer of the models to edge nodes for prediction in Kubernetes environment. Additionally, predictors at the edge nodes can be automatically updated without interrupting operation. Performance results with sensor-based devices (Raspberry Pi 4 and Jetson Nano) indicated that a satisfactory prediction latency (~7–9 s) can be achieved within the research context. However, model switching led to an increase in prediction latency (~9–13 s). Partial evaluation of a Reference Architecture for edge computing systems, which was used as a starting point for architecture design, may be considered as an additional contribution of the paper.

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Section: Discussionmentioning

confidence: 99%

Architecture for Enabling Edge Inference via Model Transfer from Cloud Domain in a Kubernetes Environment

et al. 2020

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“…Data integrity attacks [13,21,25,29,40,41,48,49,53,55,[59][60][61][62]70,75,76] Unusual consumption behaviors and measurements [6,24,27,32,34,35,38,46,52,67,68,[71][72][73] Network intrusions [16,18,19,56,63,69] Network infrastructure anomalies [14,15,17,20,22,33,39,47,58,64] Electrical data anomalies [7,23,26,36,…”

Section: Study Object Papermentioning

confidence: 99%

“…Big Data technologies, such as Apache (Flink, Storm, Spark), Hadoop (HDFS), and KairosDB are also useful to study unusual customer consumption behaviors and discover unexpected patterns [38]. Due to the speed at which data is generated in SGs, similar solutions will be required as part of energy management systems.…”

Section: Unusual Consumption Behaviors and Measurementsmentioning

confidence: 99%

A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence

Guato Burgos,

Morato,

Vizcaino Imacaña

2024

Applied Sciences

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The size of power grids and a complex technological infrastructure with higher levels of automation, connectivity, and remote access make it necessary to be able to detect anomalies of various kinds using optimal and intelligent methods. This paper is a review of studies related to the detection of anomalies in smart grids using AI. Digital repositories were explored considering publications between the years 2011 and 2023. Iterative searches were carried out to consider studies with different approaches, propose experiments, and help identify the most applied methods. Seven objects of study related to anomalies in SG were identified: attacks on data integrity, unusual measurements and consumptions, intrusions, network infrastructure, electrical data, identification of cyber-attacks, and use of detection devices. The issues relating to cybersecurity prove to be widely studied, especially to prevent intrusions, fraud, data falsification, and uncontrolled changes in the network model. There is a clear trend towards the conformation of anomaly detection frameworks or hybrid solutions. Machine learning, regression, decision trees, deep learning, support vector machines, and neural networks are widely used. Other proposals are presented in novel forms, such as federated learning, hyperdimensional computing, and graph-based methods. More solutions are needed that do not depend on a lot of data or knowledge of the network model. The use of AI to solve SG problems is generating an evolution towards what could be called next-generation smart grids. At the end of this document is a list of acronyms and terminology.

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“…However, anomalies in smart grids may arise from multiple sources, including abnormal consumption patterns from customers, infrastructure failures, power outages, malicious network attacks, or energy theft. Examples of events that can be classified as "anomalies" include power outages, transmission line failures, abnormal power consumption, and both transient and sustained power outages [13]. As shown by [14], there are three main types of anomalies in time series data:…”

Section: Definition and Classification Of Anomalous Datamentioning

confidence: 99%

Review of Detection Methods for Abnormal Electricity Consumption Data in Smart Grid

Xiao

2024

IJCSIT

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The smart grid is an intelligent system of the power grid, which is a communication information support platform based on the coordinated development of the transmission network and various levels of power grids. It is a highly integrated system characterized by informationization, automation, and interactivity of various voltage levels, including transmission and transformation, distribution, and power dispatch. This article summarizes, analyzes, and summarizes the methods for detecting abnormal electricity consumption data in smart grids. It introduces the detection methods for abnormal electricity consumption data based on traditional technology and artificial intelligence technology, analyzes and elaborates on the basic principles and characteristics of each method, summarizes and looks forward to the challenges and future development trends faced by abnormal electricity consumption data detection in smart grids, and provides some reference for subsequent research.

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Big Data Platform for Smart Grids Power Consumption Anomaly Detection

Cited by 12 publications

References 34 publications

Architecture for Enabling Edge Inference via Model Transfer from Cloud Domain in a Kubernetes Environment

Architecture for Enabling Edge Inference via Model Transfer from Cloud Domain in a Kubernetes Environment

A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence

Review of Detection Methods for Abnormal Electricity Consumption Data in Smart Grid

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