Drift-Aware Methodology for Anomaly Detection in Smart Grid

Fenza, Giuseppe; Gallo, Mariacristina; Loia, Vincenzo

doi:10.1109/access.2019.2891315

Cited by 150 publications

(75 citation statements)

References 26 publications

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“…There is no doubt that the development of SC is closely linked to energy saving and the promotion of renewable energies [48]. The articles related to this SDGs deal with topics such as the study of energy needs and consumption [50][51][52][53][54], thermal comfort in buildings [55,56], simulations [57] and measurements by intelligent electrical systems [58].…”

Section: Relationship Sdg-sustainabilitymentioning

confidence: 99%

Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era

2020

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The unprecedented urban growth of recent years requires improved urban planning and management to make urban spaces more inclusive, safe, resilient and sustainable. Additionally, humanity faces the COVID pandemic, which especially complicates the management of Smart Cities. A possible solution to address these two problems (environmental and health) in Smart Cities may be the use of Machine Learning techniques. One of the objectives of our work is to thoroughly analyze the link between the concepts of Smart Cities, Machine Learning techniques and their applicability. In this work, an exhaustive study of the relationship between Smart Cities and the applicability of Machine Learning (ML) techniques is carried out with the aim of optimizing sustainability. For this, the ML models, analyzed from the point of view of the models, techniques and applications, are studied. The areas and dimensions of sustainability addressed are analyzed, and the Sustainable Development Goals (SDGs) are discussed. The main objective is to propose a model (EARLY) that allows us to tackle these problems in the future. An inclusive perspective on applicability, sustainability scopes and dimensions, SDGs, tools, data types and Machine Learning techniques is provided. Finally, a case study applied to an Andalusian city is presented.

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Section: Relationship Sdg-sustainabilitymentioning

confidence: 99%

Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era

2020

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“…On the other hand, Capazzoli et al [26] characterize the energy time series in time windows, which impede real-time (or near real-time) analysis. Meanwhile, other studies apply machine learning to detect outliers in electricity consumption, for instance, Jokar et al, [27] use support vector machines and unsupervised learning (k-means), while Fenza and Gallo [28] apply LSTM neural networks and statistics. In both cases, the selection of training data requires considerable effort, and the time-series analysis was not considered.…”

Section: Related Workmentioning

confidence: 99%

“…In this context, the authors have proposed a specific method to obtain patterns and detect anomalies in the electricity demand. K-means [34,35] a,c Image processing technology [36] Classification and outlier detection b,c Canonical variate analysis [25] K-means and support vector machines [21] Outlier Detection b Statistics and hierarchical clustering [24] Symbolic aggregate approximation process [26] a,b C-means based on fuzzy clustering [20] a,c Support vector machines and k-means [27] LSTM neural networks and statistics [28] [12,13] a,e Support vector regression [14] d,e Simple linear regression, multiple linear regression, and ARIMA [37] b Data mining, unsupervised data clustering and bayesian network prediction [15] Energy Management a,b Hierarchical clustering [16] a Event-triggered-based distributed algorithm [38] a,e Formulation of a multiple knapsack problem and solve it through dynamic programming [39] b…”

Section: Related Workmentioning

confidence: 99%

A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles

et al. 2020

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Electricity consumption patterns reveal energy demand behaviors and enable strategY implementation to increase efficiency using monitoring systems. However, incorrect patterns can be obtained when the time-series components of electricity demand are not considered. Hence, this research proposes a new method for handling time-series components that significantly improves the ability to obtain patterns and detect anomalies in electrical consumption profiles. Patterns are found using the proposed method and two widespread methods for handling the time-series components, in order to compare the results. Through this study, the conditions that electricity demand data must meet for making the time-series analysis useful are established. Finally, one year of real electricity consumption is analyzed for two different cases to evaluate the effect of time-series treatment in the detection of anomalies. The proposed method differentiates between periods of high or low energy demand, identifying contextual anomalies. The results indicate that it is possible to reduce time and effort involved in data analysis, and improve the reliability of monitoring systems, without adding complex procedures.

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“…In this context, existing methods mostly rely on the oneclass learning setting. Alternative methods are based on Long-Short Term Memory neural networks [21], Empirical Mode Decomposition [22], Symbolic Dynamic Filtering [23] and the Margin Setting Algorithm [24], although they focus primarily on the detection of anomalies and attacks in the smart grid, rather than generic changes.…”

Section: Introductionmentioning

confidence: 99%

ECHAD: Embedding-Based Change Detection From Multivariate Time Series in Smart Grids

et al. 2020

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Smart grids are power grids where clients may actively participate in energy production, storage and distribution. Smart grid management raises several challenges, including the possible changes and evolutions in terms of energy consumption and production, that must be taken into account in order to properly regulate the energy distribution. In this context, machine learning methods can be fruitfully adopted to support the analysis and to predict the behavior of smart grids, by exploiting the large amount of streaming data generated by sensor networks. In this paper, we propose a novel change detection method, called ECHAD (Embedding-based CHAnge Detection), that leverages embedding techniques, one-class learning, and a dynamic detection approach that incrementally updates the learned model to reflect the new data distribution. Our experiments show that ECHAD achieves optimal performances on synthetic data representing challenging scenarios. Moreover, a qualitative analysis of the results obtained on real data of a real power grid reveals the quality of the change detection of ECHAD. Specifically, a comparison with stateof-the-art approaches shows the ability of ECHAD in identifying additional relevant changes, not detected by competitors, avoiding false positive detections.

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Drift-Aware Methodology for Anomaly Detection in Smart Grid

Cited by 150 publications

References 26 publications

Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era

Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era

A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles

ECHAD: Embedding-Based Change Detection From Multivariate Time Series in Smart Grids

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