In modern power grids, the detection of electricity theft is crucial for ensuring the safety and stability of the power system and reducing revenue losses. Current electricity theft detection methods do not take into account the spectral space features contained in the original time series data sequences, and thus are unable to adapt to the complex and ever-changing scenarios of electricity theft. This paper proposes an electricity theft detection model TSFPTD that integrates time series signals and their synchronous spectral features. The multi-modal model constructs the synchronous spectral modal space corresponding to the time series data through a deep wavelet network. It is found that this newly generated synchronous modal space contains implicit features that cannot be revealed by the original time series data. The explicit features of the time series data space and the implicit features of the synchronous spectral modal space are fused and aligned for the detection of power theft behavior. The performance verification experiment of the model was completed on the real dataset released by State Grid Corporation of China. The electricity theft detection accuracy of the TSFPTD model reached over 96.83%, and its performance is superior to the existing electricity theft detection methods.

Detection method of coordinated fixed-rate electricity theft in high loss lines based on forward stepwise regression

Malicious clients participating in data collection and interaction may launch attacks such as model and data poisoning to degrade the performance of the global model and conceal their electricity theft behaviors. Although existing studies have introduced blockchain technology to achieve decentralization, they still suffer from limited pre-aggregation validation dimensions. To address these issues, this paper proposes a blockchain-based federated learning approach with dual-verification (BFL-DV) for electricity theft detection. In the pre-aggregation stage, a multi-metric reputation-based consensus committee verification strategy is designed, which effectively mitigates the impact of malicious participants. In the post-aggregation stage, a dynamic threshold-based blockchain verification strategy is developed to counter security risks during the transmission process, which can refuse malicious global updates adaptively. Experimental results demonstrate that BFL-DV can accurately reduce the impact of all malicious clients under the data poisoning attack. Notably, across various proportions of malicious clients, the proposed framework achieves an average AUC improvement of 32.68% compared with SOTA methods, demonstrating its consistent performance advantage.

摘要： 备选是指最终被组织安排承担某项任务或角色, 但并非该任务或角色首选的员工, 近年来逐渐受到学者关注。然而现有研究却忽视了任务分配情境下备选对员工自身态度和行为的影响。本研究基于社会信息加工理论和联想命题评价理论, 采用情境实验(研究1)和三阶段问卷调查(研究2), 探讨了任务分配情境下备选对员工行为的“双刃剑”效应及边界条件。研究结果表明, 当上级发展性反馈水平高时, 备选会激发和谐型激情, 进而引发主动担责行为; 当上级发展性反馈水平低时, 备选会引起工作拖延倾向, 进而发生时间窃取行为。研究结论为大众更加客观、辩证地认识备选提供参考和借鉴。

Purpose Time theft is a widespread and costly workplace deviant behavior. Based on social information processing theory, the authors build a multilevel model to explore when and how team-level high involvement work systems (HIWSs) could effectively reduce time theft behavior. Specifically, this study aims to propose that HIWSs relate to employee time theft through the mediating effect of psychological empowerment and the moderating role of team-level organizational identification. Design/methodology/approach Through a three-wave field survey, this study successfully collected data from 396 employees and their 87 direct supervisors working in different industries in an eastern province of China. Findings The results suggest that HIWSs reduce employee time theft via psychological empowerment, and team-level organizational identification strengthens the indirect effect. Originality/value This study contributes to the literature by introducing HIWSs as a human resource management-related antecedent of time theft. It also identifies psychological empowerment as a key mediator that links HIWSs to employee time theft and reveals the moderating role of organizational identification in the relationship.

Surveillance systems play a crucial role in detecting suspicious human activities, including attacks, violence, and abductions, in public spaces. This study presents a human intervention-free, hybrid framework that utilizes deep neural networks for real-time theft activity recognition. The proposed methodology employs a dual stream fusion network, combining appearance and motion features, to accurately identify theft actions. Specifically, a modified InceptionV3 model extracts relevant body pose features through keypoint transfer, feeding two separate deep neural network pipelines for appearance and motion analysis. Long-Short-Term Memory network then models temporal relationships between the extracted features across consecutive frames. The novelty of this research lies in the proposed dual-stream fusion architecture, which aims to capture fine-grained temporal and spatial cues for theft detection. A new lab-lifting dataset has also been developed to reflect subtle theft behaviors in academic settings. The framework's performance is evaluated on a dataset comprising normal and theft activities. The results demonstrate a recognition accuracy of 91.86% , surpassing that of other methods.

Heuristic algorithms are a commonly used type of algorithm in the path planning of agricultural robots. However, during the iteration process, there may be situations where one gets stuck in a local optimal solution. To solve the above problems, this paper proposes a multi-strategy improved dung beetle optimization algorithm. A raster map environment model is established to plan the path of agricultural robots. In the dung beetle algorithm, Logistics chaotic mapping is first adopted to perform chaotic initialization on Zhongchun, making the population distribution more uniform in the search space and improving the search quality of the population. Secondly, the Levy flight strategy is introduced in the theft behavior to enhance the search ability and diversity of the algorithm and reduce the probability of getting stuck in the local optimal solution. A raster map model for simulating the field working environment was constructed using Matlab. Comparative experiments were conducted among the improved dung beetle algorithm, the original dung beetle algorithm, the sparrow optimization algorithm, and the goose flock optimization algorithm. The results show that the improved dung beetle algorithm has more outstanding performance in terms of path length, number of iterations, and stability.

In response to the increasingly concealed and sophisticated methods of electricity theft, which are difficult to comprehensively cover and detect in a timely manner, a method for identifying high-risk electricity theft behaviors in low-voltage distribution station areas based on density-based clustering of IoT sensing data is investigated. An intelligent IoT power distribution terminal is deployed at the distribution transformer side within the station area to collect IoT sensor data reflecting electricity consumption behavior. The density-based clustering algorithm is employed to achieve comprehensive clustering of the IoT sensing data by determining the initial cluster centers and iteratively searching and updating these centers. The clustering results of the IoT sensing data are used as input to an LM-BP neural network, which classifies the electricity consumption behavior data in the station area into normal and abnormal categories. Based on optimal matching values, a feature matching approach is applied to determine whether abnormal electricity consumption samples correspond to high-risk theft behaviors, thereby enabling the detection of such behaviors in low-voltage distribution station areas. Experimental results demonstrate that the proposed method can accurately identify high-risk electricity theft behaviors, such as meter bypassing, by leveraging the density-based clustering results of IoT sensing data.

My coworkers tell me they are paid more than me! The I-EDM model perspective on time theft behaviors

In order to solve the problem that the existing methods of electricity theft detection in dedicated user photovoltaic systems are difficult to capture subtle anomalies in non-stationary electricity consumption data, this paper introduces a method combining wavelet transform and support vector machine (WT-SVM). The Daubechies wavelet basis function is used to perform multi-scale decomposition of photovoltaic electricity consumption data, extract time-frequency features, and capture transient anomalies in electricity theft behavior. The extracted features are input into the SVM classification model, and the model is trained through the RBF kernel function. Grid search and cross-validation are used to optimize hyperparameters to improve the generalization ability of the model.The results show that under the same photovoltaic power theft detection dataset and test environment, the WT-SVM in this paper extracts time-frequency features through multi-scale wavelet decomposition and combines RBF (Radial Basis Function) and SVM classification, achieving an F1 score of 94.5%, a low latency of 35ms and a noise resistance of 91.2%, and outperforms the comparison model (Time-Freq Transformer: 62.4MB; MobileNetst: 5.7MB) with a lightweight of 2.1MB. The method in this paper has a good recognition effect on electricity theft behaviors such as current bypass, inverter tampering, and data injection, verifies the effectiveness of the fusion of wavelet time-frequency analysis and machine learning, and provides a high-precision and high-practicality solution for electricity theft detection in photovoltaic systems.

stimation method based on semi-supervised learning and time series analysis prediction. The electricity consumption data of power theft users are extracted as time series data, and in order to achieve multi-step prediction, MMD is utilized to improve the LSSVR semi-supervised learning algorithm. In addition, a perturbation term is introduced to optimize the convergence effect of the artificial bee colony algorithm, and a time series prediction algorithm based on improved artificial bee colony is established. Bringing in the power theft monitoring process to identify whether the user has power theft behavior, using the real power consumption dataset as the experimental validation data, comparing the identification accuracy of the prediction model. Predict the potential power theft of each user, solve the optimization model with the goal of optimal economic efficiency, and determine the actual ranking order of power theft users. The improved time series prediction algorithm proposed in this paper has a global error of 0.0003 and 0.0027 in dataset 1 and dataset 2, respectively, with the lowest global error and the highest overall accuracy of PSE prediction. And the algorithm predicts the list of users to be scheduled is basically the same as the list of users determined by the real PSE, which can achieve the maximum economic benefits.

ABSTRACTDespite the growing prevalence of telework in the workplace, the impact of telework extent on deviant workplace behaviors, especially time theft, has received scant research attention. Notwithstanding common assumptions, Microsoft and Ctrip have demonstrated in practice that telework does not necessarily lead to time theft among teleworkers. Inspired by these insights, the current research leverages the theoretical perspective of moral self‐regulation, proposing that telework extent threatens moral justification and displacement of responsibility, thereby reducing time theft behavior. The strength of these above indirect effects is contingent upon the key individual moral trait of teleworkers, namely moral attentiveness. This moderated mediation model is validated through a three‐wave study involving a sample of 304 teleworkers. Implications of how telework extent influences time theft behaviors from the moral self‐regulation perspective are discussed.

Smart grids have enormous potential in terms of reliability and sustainability, but with the large-scale integration of distributed energy like solar energy, the network security risks of smart grids have also increased. In response to the physical and information network threats faced in the network security risk assessment of solar powered smart grids, this study develops a smart grid theft detection model based on TimesNet and a smart grid intrusion detection model based on bidirectional long short-term memory networks. The results indicated that when the proportion of electricity theft data was 25%, the false detection rate of the proposed model was 3.52. The area under the curve of the proposed model was 0.98, and the detection rate, false negative rate, F1 value, and accuracy were 97.04%, 1.21%, 92.69%, and 97.15%, respectively. The loss value of the proposed intrusion detection model was stable at around 0.012 in the NSL-KDD dataset and around 0.02 in the CICIDS2017 dataset, with a detection accuracy of 97.54% and a false positive rate of 1.21%. The experiment demonstrated the electricity theft behavior and network intrusion detection performance of the proposed model, which can effectively detect security threats faced by solar smart grids and provide practical basis for network security risk assessment. The research results can help reduce the economic losses of power companies, maintain a good order of electricity consumption, and ensure the safe and stable operation of solar smart grids.

The prevailing viewpoint has long depicted employee time theft as inherently detrimental. However, this perspective may stem from a limited understanding of the underlying motives that drive such behavior. Time theft can paradoxically be motivated by neutral and even laudable intentions, such as promoting work efficiency, thus rendering it potentially beneficial and constructive. Across three mixed-methods studies, we explore the motives behind employee time theft, develop and validate an instrument to assess these motives, and examine how they differentially predict time theft behavior. Specifically, in Study 1, we use a qualitative method and identify 11 types of time theft motives. Study 2 embarks on the development of measures of these motives, subsequently validating their factor structure. Study 3 examines their incremental variance in predicting time theft behavior by controlling for personality and demographic variables. Overall, these studies reveal that employees' engagement in time theft can be driven not solely by self-oriented motives but also by others- and work-oriented motives. Further, each of these motives provides incremental value in understanding time theft behavior. Implications for both research and practice emanating from these findings are also discussed. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Abstract To address the key issues of low accuracy and high false alarm rate in electricity theft detection in smart grids, this paper proposes an innovative detection method based on the deep integration of convolutional neural network and Kalman filter. Firstly, according to the data structure of the power system, the learning model based on neural network is constructed, and the data processing, feature extraction, and anomaly detection are carried out on the convolution layer and the pooling layer. Secondly, design a Kalman filter, linearize the nonlinear measurement function output by convolutional neural network using the Jacobian matrix, and achieve accurate state estimation through the iterative square root of the state covariance matrix. Thirdly, establish a joint decision-making mechanism of convolutional neural network feature space and Kalman state space to achieve dynamic detection and prediction update of electricity theft behavior, breaking through the single limitations of existing methods in feature representation or noise processing, and achieving the organic combination of feature learning ability and state estimation ability. Compared with the traditional detection methods, the accuracy of the proposed method was 98.9% and the false-positive rate was 0.71%, this improves the detection accuracy of electric theft and the robustness of the system, providing reliable support for the safe operation of the power grid.

In order to improve the accuracy of electricity theft detection in low-voltage substations, a method of electricity theft detection based on multi-source data fusion is proposed and designed. Firstly, a structure design of power load data collection is designed to obtain multi-source power stealing behaviour data in low-voltage power station area. Then, K-means algorithm is used to extract the features of multi-source power theft data, and feature superposition method is used to complete the feature fusion of multi-source power theft data in low-voltage power station area. Finally, the integrated characteristic vector of electricity theft behaviour is used as input to design the electricity theft detection based on improved support vector machine (SVM) algorithm. The experimental results show that the method proposed in this paper can greatly improve the detection accuracy, and is better than the comparison method.

A detection method for electricity theft behavior in low-voltage power stations: multi-source data fusion

The weakening of the regional economy is often a complex social problem. Criminal behavior can occur such as theft of residents' houses caused by the weakening of the regional economy. The study was conducted in Merangin Regency, Jambi Province, which is the second largest palm oil producing province after Riau Province. Merangin is a district with the largest palm oil productivity in Jambi Province. Merangin Regency's dependence on palm oil production is the reason why this location is suitable for research. This study is included in qualitative research with a phenomenological approach that examines precisely and in depth the influence of the regional economy on criminal theft behavior in Merangin Regency, Jambi Province. The weakening of the regional economy due to dependence on income from the weakening of palm oil production and fluctuations in the price of determining Fresh Fruit Bunches (FFB) which have an impact on criminal behavior such as theft. This is caused by the loss of people's fixed income and the influence of other factors such as drug consumption which requires perpetrators to fulfill their needs by stealing. The weakening of the local economy also causes low microeconomic resilience because the fulfillment of household needs is reduced and income is also reduced. Meanwhile, national security, which is a function of the police institution, is also disrupted because negative assumptions about the police are developing in society, resulting in feelings of reluctance and fear of reporting to the police if a criminal act of theft occurs in Merangin Regency.

The main objective of this study was to understand why employees engage in time theft behavior and what is the behavioral consequence of this deviant behavior. To do this, the conservation of resources theory helps to examine the role of organizational cronyism behind employee time theft behavior and decreased proactive behavior. A three-wave self-administered employee survey was used for data collection. The data were collected through an adopted questionnaire from nurses working in the public sector hospitals of Pakistan. Structural equation modeling (SEM) was used to analyze data collected from 256 respondents. The results of this three-wave study supported the hypotheses which are: (1) Organizational cronyism positively predicts employee involvement in time theft behavior. (2) Employee time theft behavior negatively impacts their proactive behavior. (3) Organizational cronyism is detrimental to employee proactive behavior. (4) The relationship between organizational cronyism and proactive behavior is mediated by time theft. In the presence of organizational cronyism, employees use time theft as a dysfunctional coping strategy to conserve their valued resources rather than allowing the organization to consume them. Organizational leaders of public sector hospitals must promote merit-based HRM practices to discourage time theft behavior as well as to improve the proactive performances of the nurses. This study is one of the initial attempts to extend the scant literature on the antecedents and consequences of time theft behavior and its dimensions in the South Asian context.

The purpose of the study was to examine counter productive work behavior and organizational productivity in selected public organizations. A study of Calabar, Ugep and Ikom local government council in Cross River State. The specific objectives were to: examine the extent to which sabotage behavior can affect organizational productivity; examine how theft can affect organizational productivity; and to determine how withdrawal can affect organizational productivity. This study adopted theoretical review methods. Information was gathered using textbooks, journals, published and unpublished journals, libraries and internet applications. Based on the theoretical review, the following findings were revealed thus: there was a significant relationship between sabotage behavior and organizational productivity, there was a significant relationship between theft behavior and organizational productivity, and there was a significant relationship between withdrawal behavior and organizational productivity. In line with the findings, the study recommended that organizations should organise regular enlightenment programmes and formulate appropriate HR policies that would help reduce the level of counterproductive work behaviors among employees. Managers should endeavor to limit the effect and pervasiveness of detrimental behaviors.