Anomaly Prediction in Electricity Consumption Using a Combination of Machine Learning Techniques

Elhadad, Rawan Mohammed; Tan, Yi-Fei; Tan, Wooi-Haw

doi:10.14716/ijtech.v13i6.5931

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…Techniques" was proposed by [7]. In this model, the Isolation Forest algorithm is used to classify smart meter readings of power usage as normal or abnormal.…”

Section: "Anomaly Prediction In Electricity Consumption Using a Combi...mentioning

confidence: 99%

Systematic Review of Models Usedto Handle Class Imbalance in Anomaly Detection for Energy Consumption

Marangu,

Njenga,

Ndung’u

2024

IJAIA

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The widespread integration of Smart technologies into energy consumption systems has brought about a transformative shift in monitoring and managing electricity usage. The imbalanced nature of anomaly data often results in suboptimal performance in detecting rare anomalies. This literature review analyzes models designed to address this challenge. The methodology involves a systematic literature review based on the five-step framework proposed by Khan, encompassing framing research questions, identifying relevant literature, assessing article quality, conducting a critical review, and interpreting results. The findings show that classical machine learning models like Support Vector Machines (SVM) and Random Forests (RF) are commonly used. In conclusion, classical machine learning models like SVM and RF struggle to recognize rare anomalies, while deep learning models, notably Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), show promise for automatically learning elaborate representations and improving performance while dealing with class imbalance.

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“…Techniques" was proposed by [7]. In this model, the Isolation Forest algorithm is used to classify smart meter readings of power usage as normal or abnormal.…”

Section: "Anomaly Prediction In Electricity Consumption Using a Combi...mentioning

confidence: 99%

Systematic Review of Models Usedto Handle Class Imbalance in Anomaly Detection for Energy Consumption

Marangu,

Njenga,

Ndung’u

2024

IJAIA

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show abstract

“…In contrast, in [40], the authors use an unsupervised approach, providing a series of unlabeled data instances as input to an Isolation Forest algorithm. This overcomes the problem of having to label a large amount of data by hand, reducing work and training time and allowing the algorithm to continue learning during the field deployment phase.…”

Section: B Literature Reviewmentioning

confidence: 99%

A Novel Methodology for Unsupervised Anomaly Detection in Industrial Electrical Systems

Carratù,

Gallo,

Iacono

et al. 2023

IEEE Trans. Instrum. Meas.

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The recent development of highly automated machinery and intelligent industrial plants has increasingly enabled the continuous monitoring of their efficiency and condition, with the aim of maintaining high production efficiency and minimal malfunctions. Typical condition monitoring and fault detection applications are often achieved using acoustic and vibrational techniques, but the availability of distributed electrical measurements opens new opportunities for industrial fault detection with minimal impact on electrical systems. Even if artificial intelligence-based approaches can be used to model industrial equipment by means of measures made on electrical systems to which they are connected, machine learning algorithms have been demonstrated to be particularly adequate for this purpose due to the huge amount of data produced by interconnected sensors and devices. In this context, the aim of this work is to propose a new unsupervised analysis methodology for detecting anomalies in industrial machinery using electrical current values and other parameters measured on the power grid. The proposed framework is aimed at incorporating the advantages of Machine Learning algorithms and those of traditional analysis, optimizing their operation to improve performance and execution time; this also incorporates a methodology for analyzing the temporal dynamics of the anomaly based on Short-time Fourier transform to strengthen the performance of the detection. The results obtained showed excellent performance, both compared to the evaluations of a technical expert and to other methodologies used in the literature, with zero false positives detected in all datasets tested and a negligible number of undetected outlier events, less than 4% of the total in the datasets.

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“…The future operation and management of power systems demand quicker decisionmaking and adaptability to unpredictability. There is a rising need for calibration and verification estimates in a variety of applications, including economic power production distribution, energy trading and system security assessments, optimal power exchange across grids, unit commitment, and performance monitoring (El-Hadad, Tan and Tan, 2022;Ul-Asar, Hassnain, and Khan, 2007).…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Utilizing a Combination Model: A Brief Review

Ahmad,

Liu,

Hashim

et al. 2024

IJTech

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To deliver electricity to customers safely and economically, power companies encounter numerous economic and technical challenges in their operations. Power flow analysis, planning, and control of power systems stand out among these issues. Over the last several years, one of the most developing study topics in this vital and demanding discipline has been electricity short-term load forecasting (STLF). Power system dispatching, emergency analysis, power flow analysis, planning, and maintenance all require it. This study emphasizes new research on long short-term memory (LSTM) algorithms related to particle swarm optimization (PSO) inside this area of short-term load forecasting. The paper presents an in-depth overview of hybrid networks that combine LSTM and PSO and have been effectively used for STLF. In the future, the integration of LSTM and PSO in the development of comprehensive prediction methods and techniques for multi-heterogeneous models is expected to offer significant opportunities. With an increased dataset, the utilization of advanced multi-models for comprehensive power load prediction is anticipated to achieve higher accuracy.

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Anomaly Prediction in Electricity Consumption Using a Combination of Machine Learning Techniques

Cited by 8 publications

References 19 publications

Systematic Review of Models Usedto Handle Class Imbalance in Anomaly Detection for Energy Consumption

Systematic Review of Models Usedto Handle Class Imbalance in Anomaly Detection for Energy Consumption

A Novel Methodology for Unsupervised Anomaly Detection in Industrial Electrical Systems

Short-Term Load Forecasting Utilizing a Combination Model: A Brief Review

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