Log in

Relevant bibliographies by topics / Energy anomaly detection / Journal articles

To see the other types of publications on this topic, follow the link: Energy anomaly detection.

Journal articles on the topic 'Energy anomaly detection'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Energy anomaly detection.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Jiang, Chao, Shengze Chen, Zhijing Zhang, and Rui Li. "Energy Meter Patch Resistance and Welding Spot Anomaly Detection Method Based on Machine Vision." Journal of Physics: Conference Series 2428, no. 1 (2023): 012045. http://dx.doi.org/10.1088/1742-6596/2428/1/012045.

Full text

Abstract:

Abstract Based on the problems, such as the difficulty of detecting and obtaining evidence of patch resistance replacement and welding spot anomaly in the current field of meter anomaly detection, a patch resistance and welding spot anomaly detection algorithm is proposed based on machine vision. The resistance anomaly detection algorithm combines the K-D tree and Ransac to complete the high-efficiency energy meter registration. It detects the suspected resistance abnormal area through the difference shadow method and then judges the resistance abnormal situation according to the resistance va

APA, Harvard, Vancouver, ISO, and other styles

2

Zhang, Zhe, Yuhao Chen, Huixue Wang, Qiming Fu, Jianping Chen, and You Lu. "Anomaly detection method for building energy consumption in multivariate time series based on graph attention mechanism." PLOS ONE 18, no. 6 (2023): e0286770. http://dx.doi.org/10.1371/journal.pone.0286770.

Full text

Abstract:

A critical issue in intelligent building control is detecting energy consumption anomalies based on intelligent device status data. The building field is plagued by energy consumption anomalies caused by a number of factors, many of which are associated with one another in apparent temporal relationships. For the detection of abnormalities, most traditional detection methods rely solely on a single variable of energy consumption data and its time series changes. Therefore, they are unable to examine the correlation between the multiple characteristic factors that affect energy consumption anom

APA, Harvard, Vancouver, ISO, and other styles

3

Hu, Min, Fan Zhang, and Huiming Wu. "Anomaly Detection and Identification Method for Shield Tunneling Based on Energy Consumption Perspective." Applied Sciences 14, no. 5 (2024): 2202. http://dx.doi.org/10.3390/app14052202.

Full text

Abstract:

Various abnormal scenarios might occur during the shield tunneling process, which have an impact on construction efficiency and safety. Existing research on shield tunneling construction anomaly detection typically designs models based on the characteristics of a specific anomaly, so the scenarios of anomalies that can be detected are limited. Therefore, the research objective of this article is to establish an accurate anomaly detection model with generalization and identification capabilities on multiple types of abnormal scenarios. Inspired by energy dissipation theory, this paper innovativ

APA, Harvard, Vancouver, ISO, and other styles

4

Jin, Hyeonseok, and Kyungbaek Kim. "TCN-USAD for Anomaly Power Detection." Korean Institute of Smart Media 13, no. 7 (2024): 9–17. http://dx.doi.org/10.30693/smj.2024.13.7.9.

Full text

Abstract:

Due to the increase in energy consumption, and eco-friendly policies, there is a need for efficient energy consumption in buildings. Anomaly power detection based on deep learning are being used. Because of the difficulty in collecting anomaly data, anomaly detection is performed using reconstruction error with a Recurrent Neural Network(RNN) based autoencoder. However, there are some limitations such as the long time required to fully learn temporal features and its sensitivity to noise in the train data. To overcome these limitations, this paper proposes the TCN-USAD, combined with Temporal

APA, Harvard, Vancouver, ISO, and other styles

5

Ko, Hoon, Kwangcheol Rim, and Isabel Praça. "Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System." Sensors 21, no. 12 (2021): 4237. http://dx.doi.org/10.3390/s21124237.

Full text

Abstract:

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not requir

APA, Harvard, Vancouver, ISO, and other styles

6

Savran, Efe, Esin Karpat, and Fatih Karpat. "Energy-Efficient Anomaly Detection and Chaoticity in Electric Vehicle Driving Behavior." Sensors 24, no. 17 (2024): 5628. http://dx.doi.org/10.3390/s24175628.

Full text

Abstract:

Detection of abnormal situations in mobile systems not only provides predictions about risky situations but also has the potential to increase energy efficiency. In this study, two real-world drives of a battery electric vehicle and unsupervised hybrid anomaly detection approaches were developed. The anomaly detection performances of hybrid models created with the combination of Long Short-Term Memory (LSTM)-Autoencoder, the Local Outlier Factor (LOF), and the Mahalanobis distance were evaluated with the silhouette score, Davies–Bouldin index, and Calinski–Harabasz index, and the potential ene

APA, Harvard, Vancouver, ISO, and other styles

7

Xiong, Zhangming, Daofei Zhu, Dafang Liu, Shujing He, and Luo Zhao. "Anomaly Detection of Metallurgical Energy Data Based on iForest-AE." Applied Sciences 12, no. 19 (2022): 9977. http://dx.doi.org/10.3390/app12199977.

Full text

Abstract:

With the proliferation of the Internet of Things, a large amount of data is generated constantly by industrial systems, corresponding in many cases to critical tasks. It is particularly important to detect abnormal data to ensure the accuracy of data. Aiming at the problem that the training data are contaminated with anomalies in autoencoder-based anomaly detection, which makes it difficult to distinguish abnormal data from normal data, this paper proposes a data anomaly detection method that combines an isolated forest (iForest) and autoencoder algorithm. In this method (iForest-AE), the iFor

APA, Harvard, Vancouver, ISO, and other styles

8

Do, Kien, Truyen Tran, and Svetha Venkatesh. "Energy-based anomaly detection for mixed data." Knowledge and Information Systems 57, no. 2 (2018): 413–35. http://dx.doi.org/10.1007/s10115-018-1168-z.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Zheng, Jianbo, Chao Yang, Tairui Zhang, et al. "Dynamic Spectral Graph Anomaly Detection." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 13410–18. https://doi.org/10.1609/aaai.v39i12.33464.

Full text

Abstract:

Graph anomaly detection is crucial for identifying anomalous nodes within graphs and addressing applications like financial fraud detection and social spam detection. Recent spectral graph neural network methods advance graph anomaly detection by focusing on anomalies that notably affect the distribution of graph spectral energy. Such spectrum-based methods rely on two steps: graph wavelet extraction and feature fusion. However, both steps are hand-designed, capturing incomprehensive anomaly information of wavelet-specific features and resulting in their inconsistent feature fusion. To address

APA, Harvard, Vancouver, ISO, and other styles

10

Li, Jun, Yongbao Liu, Qiang Wang, Zhikai Xing, and Fan Zeng. "Rotating machinery anomaly detection using data reconstruction generative adversarial networks with vibration energy analysis." AIP Advances 12, no. 3 (2022): 035221. http://dx.doi.org/10.1063/5.0085354.

Full text

Abstract:

Rotating machines, such as engines, turbines, or gearboxes, are widely used in modern society. Their mechanical components, such as rotors, bearings, or gears, are the main parts, and any failure in them can lead to a complete shutdown of the rotating machinery. Anomaly detection in such critical systems is essential for the healthy operation of rotating machinery. As the requirement of obtaining sufficient fault data of rotating machinery is challenging to satisfy, a new anomaly detection model is proposed for rotating machinery, which can achieve anomaly detection without fault samples. The

APA, Harvard, Vancouver, ISO, and other styles

11

Kulkarni, Keyur. "Anomaly Detection in Smart Electric Meters for Detecting Faults and Misuse of Electric Energy Consumption." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34689.

Full text

Abstract:

The emergence of Smart Electric Meters (SEMs) has revolutionized energy management, providing real-time data collection and monitoring capabilities. However, ensuring the accuracy and security of this data presents challenges. Anomaly detection in electric energy consumption patterns is crucial for identifying issues like technical faults, erroneous billing, and misuse of electricity. Our project offers an Anomaly Detection System that leverages data analytics and machine learning to scrutinize SEM data. By analyzing historical patterns, the system distinguishes anomalies from routine fluctuat

APA, Harvard, Vancouver, ISO, and other styles

12

Wirawan, I. Made, Aji Prasetya Wibawa, and Triyanna Widiyanintyas. "Photovoltaic Energy Anomaly Detection using Transformer Based Machine Learning." International Journal of Robotics and Control Systems 4, no. 3 (2024): 1337–52. http://dx.doi.org/10.31763/ijrcs.v4i3.1260.

Full text

Abstract:

This study uses the Anomaly Transformer model to find anomalies in photovoltaic energy generation in Malang, Indonesia. The main background of this study is the lack of satellite monitoring in this region and the importance of annual data for electricity generation forecasting. Temperature scattered direct solar radiation, and hourly electricity production are all part of the dataset used which is only available since 2019. Anomalies were detected at 05.00 and 16.00 WIB, indicating instability in the energy supply due to high temperatures in the morning and heavy rain in the afternoon. Detecti

APA, Harvard, Vancouver, ISO, and other styles

13

IMRAN, T. "Anomaly Detection in ELI-NP Front-End Laser Energy Data Using an Optimized Moving Average Method." Romanian Journal of Physics 70, no. 3-4 (2025): 902. https://doi.org/10.59277/romjphys.2025.70.902.

Full text

Abstract:

Anomaly detection in time-series data is critical for ensuring stability in high-power laser systems, where deviations can indicate potential failures. This study optimizes a moving average-based methodology for anomaly detection accuracy by evaluating window sizes (3, 6, 9, 12, and 15) and threshold multipliers (1.0, 1.5, and 2.0). The analysis integrates Mean Squared Error (MSE), correlation analysis, and graphical evaluations, including anomaly distribution, moving average trends, and parameter sensitivity plots. Results indicate that smaller window sizes effectively detect short-term fluct

APA, Harvard, Vancouver, ISO, and other styles

14

Zhu, Yong, Mingyi Liu, Xiaowei Hao, et al. "Multi-level anomaly detection of lithium battery energy storage system." Journal of Physics: Conference Series 2788, no. 1 (2024): 012040. http://dx.doi.org/10.1088/1742-6596/2788/1/012040.

Full text

Abstract:

Abstract The energy storage technology route represented by lithium battery energy storage strongly supports China’s energy structure transformation. The widespread use of lithium batteries also poses a significant safety risk that is often overlooked. Energy storage system security is facing severe challenges. It is very beneficial for the safety of energy storage systems to predict the potential faults of lithium batteries before they are used and find anomaly batteries in time. This paper proposes a three-stage anomaly detection method based on statistics and density concepts to provide rea

APA, Harvard, Vancouver, ISO, and other styles

15

Wen-Jen Ho, Wen-Jen Ho, Hsin-Yuan Hsieh Wen-Jen Ho, and Chia-Wei Tsai Hsin-Yuan Hsieh. "Anomaly Detection Model of Time Segment Power Usage Behavior Using Unsupervised Learning." 網際網路技術學刊 25, no. 3 (2024): 455–63. http://dx.doi.org/10.53106/160792642024052503011.

Full text

Abstract:

<p>In Taiwan, the current electricity prices for residential users remain relatively low. This results in a diminished incentive for these users to invest in energy-saving improvements. Consequently, devising strategies to encourage residential users to adopt energy-saving measures becomes a vital research area. Grounded in behavioral science, this study introduces a feasible approach where an energy management system provides alerts and corresponding energy-saving recommendations to residential users upon detecting abnormal electricity consumption behavior. To pinpoint anomalous electri

APA, Harvard, Vancouver, ISO, and other styles

16

Solís-Villarreal, José-Alberto, Valeria Soto-Mendoza, Jesús Alejandro Navarro-Acosta, and Efraín Ruiz-y-Ruiz. "Energy Consumption Outlier Detection with AI Models in Modern Cities: A Case Study from North-Eastern Mexico." Algorithms 17, no. 8 (2024): 322. http://dx.doi.org/10.3390/a17080322.

Full text

Abstract:

The development of smart cities will require the construction of smart buildings. Smart buildings will demand the incorporation of elements for efficient monitoring and control of electrical consumption. The development of efficient AI algorithms is needed to generate more accurate electricity consumption predictions; therefore; anomaly detection in electricity consumption predictions has become an important research topic. This work focuses on the study of the detection of anomalies in domestic electrical consumption in Mexico. A predictive machine learning model of future electricity consump

APA, Harvard, Vancouver, ISO, and other styles

17

Anitha Kumari, K., Avinash Sharma, R. Barani Priyanga, and A. Kevin Paul. "ENERGY DATA ANOMALY DETECTION USING UNSUPERVISED LEARNING TECHNIQUES." Advances in Mathematics: Scientific Journal 9, no. 9 (2020): 6687–98. http://dx.doi.org/10.37418/amsj.9.9.26.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Salma, Volkan, and Roland Schmehl. "Flight Anomaly Detection for Airborne Wind Energy Systems." Journal of Physics: Conference Series 1618 (September 2020): 032021. http://dx.doi.org/10.1088/1742-6596/1618/3/032021.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Xu, Xiang, Zhen-Dong Qian, Qiao Huang, Yuan Ren, and Bin Liu. "Probabilistic anomaly trend detection for cable-supported bridges using confidence interval estimation." Advances in Structural Engineering 25, no. 5 (2022): 966–78. http://dx.doi.org/10.1177/13694332211056108.

Full text

Abstract:

To rate uncertainties within anomaly detection course for large span cable-supported bridges, a probabilistic approach is developed based on confidence interval estimation of extreme value analytics. First, raw signals from structural health monitoring system are pre-processed, including missing data imputation using moving time window mean imputation approach and thermal response separation through multi-resolution wavelet-based method. Then, an energy index is extracted from time domain signals to enhance robust of detection performance. A resampling-based method, namely the bootstrap, is ad

APA, Harvard, Vancouver, ISO, and other styles

20

Yu, Zhuchao, Gaixia Chu, Yuanzheng Zhang, Siyuan Zhou, Shaowei He, and Jing Li. "Abnormal Detection Model of Energy Consumption Data in Beneficiation and Metallurgy Enterprises based on Transfer Learning." Journal of Physics: Conference Series 2205, no. 1 (2022): 012012. http://dx.doi.org/10.1088/1742-6596/2205/1/012012.

Full text

Abstract:

Abstract The energy consumption data of beneficiation and metallurgy enterprises has the characteristics of multi-dimensional and timing. An energy consumption data anomaly detection model based on transfer learning is proposed to help enterprises monitor the abnormal energy use. The combination of transfer learning and DTW algorithm is used to eliminate the adverse impact of timing on the model, and the ensemble learning is used to realize data detection. Finally, an efficient and accurate energy consumption data anomaly detection model is formed. The detection accuracy of the model is 91.6%,

APA, Harvard, Vancouver, ISO, and other styles

21

Deng, Shuixin, Lei Deng, Ting Sun, et al. "EEDD: Edge-Guided Energy-Based PCB Defect Detection." Electronics 12, no. 10 (2023): 2306. http://dx.doi.org/10.3390/electronics12102306.

Full text

Abstract:

Printed circuit board (PCB) defect detection is critical for ensuring the safety of electronic devices, especially in the space industry. Non-reference-based methods, typically the deep learning methods, suffer from a large amount of annotated data requirements and poor interpretability. In contrast, conventional reference-based methods achieve higher detection accuracy by comparing with a template image but rely on precise image alignment and face the challenge of fine defects detection. To solve the problem, we propose a novel Edge-guided Energy-based PCB Defect Detection method (EEDD). We f

APA, Harvard, Vancouver, ISO, and other styles

22

Gayathri, B., A. Abhinav, C. Harishwar Reddy, and G. Praneeth. "ANOMALY XPERT: A Deep Learning Approach to Anomaly Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 1481–85. http://dx.doi.org/10.22214/ijraset.2024.59081.

Full text

Abstract:

Abstract: In response to increasing crime rates, organizations are deploying surveillance systems with CCTV cameras to detect suspicious activities autonomously. This paper proposes an automated system using transfer learning-based CNN models to track and classify activities like 'Shoplifting,' 'Robbery,' or 'Break-In' in real-time CCTV footage. The framework processes raw camera data, detects objects, tracks activities, and classifies them, generating alerts for authorized personnel. Leveraging transfer learning enhances the precision and effectiveness of the CNN model in identifying security

APA, Harvard, Vancouver, ISO, and other styles

23

Saganowski, Łukasz, and Tomasz Andrysiak. "Snort IDS Hybrid ADS Preprocessor." Image Processing & Communications 17, no. 4 (2012): 17–22. http://dx.doi.org/10.2478/v10248-012-0024-0.

Full text

Abstract:

Abstract The paper presents hybrid anomaly detection preprocessor for SNORT IDS - Intrusion Detection System [1] base on statistical test and DWT - Discrete Wavelet Transform coefficient analysis. Preprocessor increases functionality of SNORT IDS system and has complementary properties. Possibility of detection network anomalies is increased by using two different algorithms. SNORT captures network traffic features which are used by ADS (Anomaly Detection System) preprocessor for detecting anomalies. Chi-square statistical test and DWT subband coefficients energy values are used for calculatin

APA, Harvard, Vancouver, ISO, and other styles

24

Branco, Pedro, Francisco Gonçalves, and Ana Cristina Costa. "Tailored Algorithms for Anomaly Detection in Photovoltaic Systems." Energies 13, no. 1 (2020): 225. http://dx.doi.org/10.3390/en13010225.

Full text

Abstract:

The fastest-growing renewable source of energy is solar photovoltaic (PV) energy, which is likely to become the largest electricity source in the world by 2050. In order to be a viable alternative energy source, PV systems should maximise their efficiency and operate flawlessly. However, in practice, many PV systems do not operate at their full capacity due to several types of anomalies. We propose tailored algorithms for the detection of different PV system anomalies, including suboptimal orientation, daytime and sunrise/sunset shading, brief and sustained daytime zero-production, and low max

APA, Harvard, Vancouver, ISO, and other styles

25

Pan, Haipeng, Zhongqian Yin, and Xianzhi Jiang. "High-Dimensional Energy Consumption Anomaly Detection: A Deep Learning-Based Method for Detecting Anomalies." Energies 15, no. 17 (2022): 6139. http://dx.doi.org/10.3390/en15176139.

Full text

Abstract:

With the increase of energy demand, energy wasteful behavior is inevitable. To reduce energy waste, it is crucial to understand users’ electricity consumption habits and detect abnormal usage behavior in a timely manner. This study proposes a high-dimensional energy consumption anomaly detection method based on deep learning. The method uses high-dimensional energy consumption related data to predict users’ electricity consumption in real time and for anomaly detection. The test results of the method on a publicly available dataset show that it can effectively detect abnormal electricity usage

APA, Harvard, Vancouver, ISO, and other styles

26

Naz, Laviza Falak, Rohail Qamar, Raheela Asif, Saman Hina, Muhammad Imran, and Saad Ahmed. "Intelligent energy management in IoT-enabled smart homes: Anomaly detection and consumption prediction for energy-efficient usage." Mehran University Research Journal of Engineering and Technology 44, no. 1 (2025): 113. https://doi.org/10.22581/muet1982.3291.

Full text

Abstract:

The increasing Internet of Things (IoT) device integration in smart home environments has increased the options available for intelligent energy management. In the context of smart homes, this paper provides a detailed analysis on the use of IoT data for energy consumption trend prediction and anomaly detection. We propose a novel approach that combines the advantages of the Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models for accurate consumption forecasting. Real-world data from a smart home setting is utilised to evaluate the proposed models. Results

APA, Harvard, Vancouver, ISO, and other styles

27

Liu, Dan, DongHong Huang, Xinyu Ye, and XinLi Dong. "Anomaly identification model of gas boiler heating energy based on anomaly detection algorithm." Journal of Physics: Conference Series 2030, no. 1 (2021): 012045. http://dx.doi.org/10.1088/1742-6596/2030/1/012045.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Song, Chuancheng, Xixun Lin, Hanyang Shen, Yanmin Shang, and Yanan Cao. "UniFORM: Towards Unified Framework for Anomaly Detection on Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 12559–67. https://doi.org/10.1609/aaai.v39i12.33369.

Full text

Abstract:

Graph anomaly detection has attracted significant attention due to its critical applications, such as identifying money laundering in financial systems and detecting fake reviews on social networks. However, two major challenges persist: (1) anomaly detection at the node, edge, and graph levels is often addressed in isolation, hindering the integration of complementary information to identify anomalies arising from collective behaviors; and (2) the inherent label sparsity in graph data, coupled with the difficulty of obtaining high-quality annotations, exacerbates bias in detection. To address

APA, Harvard, Vancouver, ISO, and other styles

29

Guo, Wei, Shengbo Sun, Chenkang Tang, Gang Li, Xinlei Bai, and Zhenbing Zhao. "Classification of Anomaly Patterns in Integrated Energy Systems Based on Conditional Variational Autoencoder and Attention Mechanism." Energies 16, no. 11 (2023): 4367. http://dx.doi.org/10.3390/en16114367.

Full text

Abstract:

By studying the classification of anomaly patterns in integrated energy systems, a deeper understanding of their operational status can be gained, leading to improved reliability and efficiency. This can ultimately result in reduced energy consumption and carbon emissions, contributing to sustainability efforts. This paper proposes a classification method that employs a conditional variational autoencoder and attention mechanism for deep clustering to identify anomaly patterns and distinguish between normal and anomaly datasets. The proposed model effectively addresses the issue of detecting a

APA, Harvard, Vancouver, ISO, and other styles

30

Cooper, Austin, Arturo Bretas, and Sean Meyn. "Anomaly Detection in Power System State Estimation: Review and New Directions." Energies 16, no. 18 (2023): 6678. http://dx.doi.org/10.3390/en16186678.

Full text

Abstract:

Foundational and state-of-the-art anomaly-detection methods through power system state estimation are reviewed. Traditional components for bad data detection, such as chi-square testing, residual-based methods, and hypothesis testing, are discussed to explain the motivations for recent anomaly-detection methods given the increasing complexity of power grids, energy management systems, and cyber-threats. In particular, state estimation anomaly detection based on data-driven quickest-change detection and artificial intelligence are discussed, and directions for research are suggested with partic

APA, Harvard, Vancouver, ISO, and other styles

31

Liu, Chunde, Xianli Su, and Chuanwen Li. "Edge Computing for Data Anomaly Detection of Multi-Sensors in Underground Mining." Electronics 10, no. 3 (2021): 302. http://dx.doi.org/10.3390/electronics10030302.

Full text

Abstract:

There is a growing interest in safety warning of underground mining due to the huge threat being faced by those working in underground mining. Data acquisition of sensors based on Internet of Things (IoT) is currently the main method, but the data anomaly detection and analysis of multi-sensors is a challenging task: firstly, the data that are collected by different sensors of underground mining are heterogeneous; secondly, real-time is required for the data anomaly detection of safety warning. Currently, there are many anomaly detection methods, such as traditional clustering methods K-means

APA, Harvard, Vancouver, ISO, and other styles

32

Revathy, Dr A. "TinyML Autoencoder for Transmission Line Anomaly Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44994.

Full text

Abstract:

Power distribution infrastructure is vital for reliable energy delivery but faces challenges from environmental stressors, aging components, and inefficient inspections. This paper introduces an automated anomaly detection system using image processing and deep learning to enhance transmission line inspections. The framework employs a convolutional autoencoder for real-time defect identification, detecting subtle degradation patterns missed by manual methods. A key innovation is the deployment of the optimized model on Raspberry Pi, reducing its size from 306KB to 2KB (a 99.3% reduction) witho

APA, Harvard, Vancouver, ISO, and other styles

33

Mishra, Deviprasad, Partha Roy, and Anil Mishra. "TRAP BASED ANOMALY DETECTION MECHANISM FOR WIRELESS SENSOR NETWORK." ASEAN Engineering Journal 14, no. 2 (2024): 167–72. http://dx.doi.org/10.11113/aej.v14.20997.

Full text

Abstract:

A Wireless Sensor Network (WSN) comprises compact, resource-limited devices strategically placed for data collection and transmission, adapting seamlessly across diverse sectors and managing sensitive information. Security is pivotal in these applications, where compromised sensor nodes swiftly jeopardize network integrity, especially without robust security measures. Strategies addressing node compromise center on detecting false data from compromised nodes but often lack precision in tracing the exact source, hindering effective compromised node detection. This paper introduces an inventive

APA, Harvard, Vancouver, ISO, and other styles

34

Katamoura, Suzan MohammadAli, and Mehmet Sabih Aksoy. "Anomaly Detection in Renewable Energy Big Data Using Deep Learning." International Journal of Intelligent Information Technologies 19, no. 1 (2023): 1–28. http://dx.doi.org/10.4018/ijiit.331595.

Full text

Abstract:

This work aims to review the literature on anomaly detection (AD) in renewable energy. Due to the significance of the RE data quality and sensor performance, it is crucial to ensure that the measurement device works correctly and maintains data accuracy. The review identifies the relevant studies on big data anomaly detection in the energy field and synthesizes the related techniques. Also, the study shows a need for segmentation annotations for solar system electroluminescence imagery complicating the domain development of anomaly segmentation approaches. Consequently, most processes create m

APA, Harvard, Vancouver, ISO, and other styles

35

Huang, Shaonian, Dongjun Huang, and Xinmin Zhou. "Learning Multimodal Deep Representations for Crowd Anomaly Event Detection." Mathematical Problems in Engineering 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/6323942.

Full text

Abstract:

Anomaly event detection in crowd scenes is extremely important; however, the majority of existing studies merely use hand-crafted features to detect anomalies. In this study, a novel unsupervised deep learning framework is proposed to detect anomaly events in crowded scenes. Specifically, low-level visual features, energy features, and motion map features are simultaneously extracted based on spatiotemporal energy measurements. Three convolutional restricted Boltzmann machines are trained to model the mid-level feature representation of normal patterns. Then a multimodal fusion scheme is utili

APA, Harvard, Vancouver, ISO, and other styles

36

Edelen, Jonathan P., and Christopher C. Hall. "Autoencoder Based Analysis of RF Parameters in the Fermilab Low Energy Linac." Information 12, no. 6 (2021): 238. http://dx.doi.org/10.3390/info12060238.

Full text

Abstract:

Machine learning (ML) has the potential for significant impact on the modeling, operation, and control of particle accelerators due to its ability to model nonlinear behavior, interpolate on complicated surfaces, and adapt to system changes over time. Anomaly detection in particular has been highlighted as an area where ML can significantly impact the operation of accelerators. These algorithms work by identifying subtle behaviors of key variables prior to negative events. Efforts to apply ML to anomaly detection have largely focused on subsystems such as RF cavities, superconducting magnets,

APA, Harvard, Vancouver, ISO, and other styles

37

Jadhav Girish Vasantrao. "Autoencoder Based Anomaly Detection of Electricity Theft in House Hold Consumer side of the smart grid." Journal of Information Systems Engineering and Management 10, no. 34s (2025): 989–1007. https://doi.org/10.52783/jisem.v10i34s.5905.

Full text

Abstract:

Introduction: Theft of electricity continues to be an ongoing problem with serious implications, such as loss of revenue, grid instability, decreased efficiency, and higher likelihoods of system overloads. The covert operation of this act presents a tremendous challenge to global power distribution networks, both to utility companies and consumers as energy needs and expenses keep on growing. Objectives: The objective of this study is to establish a consistent method for identifying electricity theft in a 19-bus power distribution system. The research targets the detection of energy usage anom

APA, Harvard, Vancouver, ISO, and other styles

38

Park, Cheong Hee, and Taegong Kim. "Energy Theft Detection in Advanced Metering Infrastructure Based on Anomaly Pattern Detection." Energies 13, no. 15 (2020): 3832. http://dx.doi.org/10.3390/en13153832.

Full text

Abstract:

Energy theft refers to the intentional and illegal usage of electricity by various means. A number of studies have been conducted on energy theft detection in the advanced metering infrastructure using machine learning methods. However, applying machine learning for energy theft detection has a problem in that it is difficult to obtain enough electricity theft data to train a machine learning model. In this paper, we propose a method based on anomaly pattern detection to detect electricity theft in data streams generated from smart meters. The proposed method requires only normal energy consum

APA, Harvard, Vancouver, ISO, and other styles

39

Shchetinin, Eugeny Yu, and Tatyana R. Velieva. "Detection of cyber-attacks on the power smart grids using semi-supervised deep learning models." Discrete and Continuous Models and Applied Computational Science 30, no. 3 (2022): 258–68. http://dx.doi.org/10.22363/2658-4670-2022-30-3-258-268.

Full text

Abstract:

Modern smart energy grids combine advanced information and communication technologies into traditional energy systems for a more efficient and sustainable supply of electricity, which creates vulnerabilities in their security systems that can be used by attackers to conduct cyber-attacks that cause serious consequences, such as massive power outages and infrastructure damage. Existing machine learning methods for detecting cyber-attacks in intelligent energy networks mainly use classical classification algorithms, which require data markup, which is sometimes difficult, if not impossible. This

APA, Harvard, Vancouver, ISO, and other styles

40

Wang, Lin, Shuqiao Zhou, Tianhao Zhang, Chao Guo, and Xiaojin Huang. "An Unsupervised Anomaly Detection Method for Nuclear Reactor Coolant Pumps Based on Kernel Self-Organizing Map and Bayesian Posterior Inference." Energies 18, no. 11 (2025): 2887. https://doi.org/10.3390/en18112887.

Full text

Abstract:

Effectively monitoring the operational status of reactor coolant pumps (RCPs) is crucial for enhancing the safety and stability of nuclear power operations. To address the challenges of limited interpretability and suboptimal detection performance in existing methods for detecting abnormal operating states of RCPs, this paper proposes an interpretable, unsupervised anomaly detection approach. This innovative method designs a framework that combines Kernel Self-Organizing Map (Kernel SOM) clustering with Bayesian Posterior Inference. Specifically, the proposed method uses Kernel SOM to extract

APA, Harvard, Vancouver, ISO, and other styles

41

Paul, Ankita, Md Abu Saleh Tajin, Anup Das, William M. Mongan, and Kapil R. Dandekar. "Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants." Electronics 11, no. 5 (2022): 682. http://dx.doi.org/10.3390/electronics11050682.

Full text

Abstract:

Precise monitoring of respiratory rate in premature newborn infants is essential to initiating medical interventions as required. Wired technologies can be invasive and obtrusive to the patients. We propose a deep-learning-enabled wearable monitoring system for premature newborn infants, where respiratory cessation is predicted using signals that are collected wirelessly from a non-invasive wearable Bellypatch put on the infant’s body. We propose a five-stage design pipeline involving data collection and labeling, feature scaling, deep learning model selection with hyperparameter tuning, model

APA, Harvard, Vancouver, ISO, and other styles

42

Erla, Nithin Reddy, Sai Teja Gaddi, Vasu Chillara, and Dr Vikas Maheshwari. "Anomaly detection for electric energy consumption in smart farms." International Journal of Advances in Electrical Engineering 5, no. 1 (2024): 116–21. http://dx.doi.org/10.22271/27084574.2024.v5.i1b.62.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Marchioni, Alex, Mauro Mangia, Fabio Pareschi, Riccardo Rovatti, and Gianluca Setti. "Subspace Energy Monitoring for Anomaly Detection @Sensor or @Edge." IEEE Internet of Things Journal 7, no. 8 (2020): 7575–89. http://dx.doi.org/10.1109/jiot.2020.2985912.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Chen, Tianyu, Chunping Hou, Zhipeng Wang, and Hua Chen. "Anomaly detection in crowded scenes using motion energy model." Multimedia Tools and Applications 77, no. 11 (2017): 14137–52. http://dx.doi.org/10.1007/s11042-017-5020-3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Simpson, C. J., J. R. Wilford, L. F. Macias, and R. J. Korsch. "SATELLITE DETECTION OF NATURAL HYDROCARBON SEEPAGE: PALM VALLEY GAS FIELD, AMADEUS BASIN, CENTRAL AUSTRALIA." APPEA Journal 29, no. 1 (1989): 196. http://dx.doi.org/10.1071/aj88019.

Full text

Abstract:

Digital image processing of advanced aircraft and Landsat Thematic Mapper (TM) satellite remotely sensed data over sandstones of the Palm Valley Gas Field, central Australia, showed a distinct colour anomaly about 6 km long by 1.5 km wide which is not obvious in visible wavelength imagery. Field inspection showed that the colour anomaly was characterised by different rock- weathering colour, a geobotanical anomaly, calcium carbonate precipitation within rock fractures, and different soil pH. Inorganic rock geochemistry indicates significant chemical differences in some major elements. A limite

APA, Harvard, Vancouver, ISO, and other styles

46

Satheesh, Dr Kavuri K. S. V. A., Sk Waaheda Zeenathul Quraan, G. Mercy Jasper, Ch Sai Teja, Ch Sai Vignesh, and V. Lohith. "Crowd Anomaly Detection Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 544–47. http://dx.doi.org/10.22214/ijraset.2024.59849.

Full text

Abstract:

Abstract: Surveillance videos have become crucial objects for ensuring the safety and security of crowded places such as concerts, subways, airports, and many others. In recent times, the installation of security cameras has rapidly increased in both public and private places. The complexity of finding the anomaly will increase drastically as the amount of footage increases and there may be occlusions for the analysis of the video, which consumes both time and may result in false detections. To overcome these drawbacks, we have produced an approach using machine learning algorithms. In this ap

APA, Harvard, Vancouver, ISO, and other styles

47

Guo, Qiang, Fenghe Li, Hengwen Liu, and Jin Guo. "Anomaly Detection and Root Cause Analysis for Energy Consumption of Medium and Heavy Plate: A Novel Method Based on Bayesian Neural Network with Adam Variational Inference." Algorithms 18, no. 1 (2025): 11. https://doi.org/10.3390/a18010011.

Full text

Abstract:

Anomaly detection and root cause analysis of energy consumption not only optimize energy use and improve equipment reliability but also contribute to green and low-carbon development. This paper proposes a comprehensive diagnostic framework for detecting anomalies, conducting causal analysis, and tracing root causes of energy consumption in medium and heavy plate manufacturing, integrating process mechanisms, expert knowledge, and industrial big data. First, a two-stage anomaly detection method based on box plot analysis is developed to identify energy consumption irregularities. Next, a weigh

APA, Harvard, Vancouver, ISO, and other styles

48

Mao, Zhenghui, Bijun Zhou, Jiaxuan Huang, Dandan Liu, and Qiangqiang Yang. "Research on Anomaly Detection Model for Power Consumption Data Based on Time-Series Reconstruction." Energies 17, no. 19 (2024): 4810. http://dx.doi.org/10.3390/en17194810.

Full text

Abstract:

The power consumption data in buildings can be viewed as a time series, where outliers indicate unreasonable energy usage patterns. Accurately detecting these outliers and improving energy management methods based on the findings can lead to energy savings. To detect outliers, an anomaly detection model based on time-series reconstruction, AF-GS-RandomForest, is proposed. This model comprises two modules: prediction and detection. The prediction module uses the Autoformer algorithm to build an accurate and robust predictive model for unstable nonlinear sequences, and calculates the model resid

APA, Harvard, Vancouver, ISO, and other styles

49

Chugh, Neeraj, Geetam Singh Tomar, Robin Singh Bhadoria, and Neetesh Saxena. "A Novel Anomaly Behavior Detection Scheme for Mobile Ad Hoc Networks." Electronics 10, no. 14 (2021): 1635. http://dx.doi.org/10.3390/electronics10141635.

Full text

Abstract:

To sustain the security services in a Mobile Ad Hoc Networks (MANET), applications in terms of confidentially, authentication, integrity, authorization, key management, and abnormal behavior detection/anomaly detection are significant. The implementation of a sophisticated security mechanism requires a large number of network resources that degrade network performance. In addition, routing protocols designed for MANETs should be energy efficient in order to maximize network performance. In line with this view, this work proposes a new hybrid method called the data-driven zone-based routing pro

APA, Harvard, Vancouver, ISO, and other styles

50

Dai, Yaoyang, Shukai Sun, and Liang Che. "Improved DBSCAN-based Data Anomaly Detection Approach for Battery Energy Storage Stations." Journal of Physics: Conference Series 2351, no. 1 (2022): 012025. http://dx.doi.org/10.1088/1742-6596/2351/1/012025.

Full text

Abstract:

In battery energy storage stations (BESSs), the power conversion system (PCS) as the interface between the battery and the power grid is responsible for battery charging and discharging control and grid connection. Any anomaly in the data of a PCS will threaten the security of the BESS. It is difficult to detect anomalies in real-time data because of the large scale, chaos, and small deviations between normal and abnormal values. In this paper, the density-based clustering algorithm DBSCAN is used for data anomaly detection. However, the traditional DBSCAN has a limitation in that it has diffi

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!