Electricity theft significantly impacts the reliability and sustainability of electricity services,particularly in developing regions.However,the socio-economic,infrastructural,and institutional drivers of theft remai...Electricity theft significantly impacts the reliability and sustainability of electricity services,particularly in developing regions.However,the socio-economic,infrastructural,and institutional drivers of theft remain inadequately explored.Here we examine electricity theft in Lubumbashi,Democratic Republic of Congo,focusing on its patterns,causes,and impacts on service quality.Theft rates exceeded 75%in peripheral municipalities like Katuba and Kampemba,driven by poverty,weak law enforcement,and poor infrastructure dominated by above-ground networks.In contrast,central areas like Kamalondo and Lubumbashi reported lower theft rates due to better urban planning and underground systems.We found that electricity theft directly correlates with frequent voltage fluctuations,prolonged outages,and grid overloads.Socio-economic factors,including high connection fees and poverty,emerged as primary drivers,while institutional weaknesses such as corruption and ineffective enforcement perpetuate theft.Addressing theft requires a holistic approach integrating infrastructure modernization,socio-economic reforms,and institutional strengthening.Transitioning to underground networks,providing affordable electricity access,and adopting advanced metering systems are crucial.Overall,this study highlights the systemic nature of electricity theft and provides actionable insights for improving electricity service delivery and equity in urban settings.展开更多
Generically, SCM may be said to include all activities carried out to ensure proper functioning of the supply chain. The activities included in proper management of a supply chain broadly include logistics activities,...Generically, SCM may be said to include all activities carried out to ensure proper functioning of the supply chain. The activities included in proper management of a supply chain broadly include logistics activities, planning and control of the flow of information and materials in a firm, management of relationships with other organizations and government intervention, However, crude oil theft and pipeline vandalism are established products supply chain disruptors in Nigeria which are rendering the task of running an efficient petroleum supply chain onerous. This paper aims to establish the importance of effective supply chain strategies for companies in the oil and gas industry with special focus on the Nigerian oil and gas sector and the strategies by which the state oil and gas corporation in this sector may mitigate disruptions to its supply chain. This study investigates the enhancement of supply chain strategies towards meeting the challenge of crude oil theft and pipeline vandalism, using the Nigerian National Corporation (NNPC) as a case study. Based on this study, data were collected from two sources: A summary of incident reports obtained from NNPC and an interview with personnel in the PPMC Department. Incident report refers to a report produced when accidents such as equipment failure, injury, loss of life, or fire occur at the work site. Content analysis is utilized to evaluate data obtained from interview responses, CBN financial stability reports, NDIC annual reports, circulars, banking supervision reports and implementation guidelines. The study found out that NNPC should endeavor to sustain its value chain and ward of pipeline vandals and crude oil thieves by engaging in community partnership, detailing security outfits to ensure its pipelines’ right of way and bridging. Concluded that the oil supply chain of the Nigerian National Petroleum Corporation has been plagued by disruptions in the form of crude oil theft and pipeline vandalism which has had debilitating effects on its value.展开更多
With the application of the advanced measurement infrastructure in power grids,data driven electricity theft detection methods become the primary stream for pinpointing electricity thieves.However,owing to anomaly sub...With the application of the advanced measurement infrastructure in power grids,data driven electricity theft detection methods become the primary stream for pinpointing electricity thieves.However,owing to anomaly submergence,which shows that the usage patterns of electricity thieves may not always deviate from those of normal users,the performance of the existing usage-pattern-based method could be affected.In addition,the detection results of some unsupervised learning algorithm models are abnormal degrees rather than“0-1”to ascertain whether electricity theft has occurred.The detection with fixed threshold value may lead to deviation and would not be sufficiently flexible to handle the detection for different scenes and users.To address these issues,this study proposes a new electricity theft detection method based on load shape dictionary of users.A corresponding strategy for tunable threshold is proposed to optimize the detection effect of electricity theft,and the efficacy and applicability of the proposed adaptive electricity theft detection method were verified from numerical experiments.展开更多
One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which make...One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously possible.For this purpose,we have taken historical data of energy thieves and normal users.To avoid imbalance observation,biased estimates,we applied the interpolation method.Furthermore,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.By proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time complexity.To minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark scheme.Our proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,respectively.Furthermore,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.展开更多
Electricity theft is one of the major issues in developing countries which is affecting their economy badly.Especially with the introduction of emerging technologies,this issue became more complicated.Though many new ...Electricity theft is one of the major issues in developing countries which is affecting their economy badly.Especially with the introduction of emerging technologies,this issue became more complicated.Though many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)techniques.Here,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and limitations.Three levels of taxonomy are presented to classify state-of-the-art ETD techniques.Different types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from literature.The challenges of different ETD techniques and their mitigation are suggested for future work.It is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft tracking.This can help in the prevention of energy theft,in the future,as well as for ETD.展开更多
Unauthorized use of energy is the major source of the non-technical losses of the energy in developing countries. Gas theft as a kind of energy theft is an increasing issue in a number of countries particularly in dev...Unauthorized use of energy is the major source of the non-technical losses of the energy in developing countries. Gas theft as a kind of energy theft is an increasing issue in a number of countries particularly in developing countries. This study is an attempt to address the issue of gas theft through the deployment of Geographic Information System (GIS) capabilities (Spatial Analysis) to import external factors into the current gas theft detection methods, improve data mining processes, and offer some management solutions. To achieve the intended goals in the study, two types of data sources were collected and analyzed: internal data such as reported instances of gas theft, and some customer properties, and external data such as some demographic data. In order to analyze and modeling the gas theft and the relationships between variables we used Hotspot analysis, Ordinary Least Squares regression (OLS) and Geographically Weighted Regression (GWR) analysis with ArcGIS tools. The results from clustering test indicated that the gas theft is not a random phenomenon in all areas of Tabriz and there are underlying factors. Mapping clusters by the hotspot techniques suggested the locations of clusters and areas at risk. The results of the regression analysis illustrated the importance of external factors clearly. According to the results, we recommend a conceptual GIS framework to select high risk areas as a subset data for a meter data analysis. Results of this research are of great importance for GIS based spatial analysis and can be used as base of future researches.展开更多
The article investigates the similarities and differences between all versions of Grand Theft Auto as an adventure game with the widest popularity in the last decade. The game is a story collection, a frame for perfor...The article investigates the similarities and differences between all versions of Grand Theft Auto as an adventure game with the widest popularity in the last decade. The game is a story collection, a frame for performance, a virtual museum of vernacular culture and a widely circulated pop culture artifact whose double-voiced aesthetic has given rise to diverse interpretive communities. The aim of comparing the differences and similarities between different versions of the game is to be able to evaluate the game from the user’s point of view. With this aim, whether with the verisimilitude that the different versions offer makes GTA a product of an iterative design process or not will be displayed.展开更多
In the field of vision of American Literature in the 20th century,Katherine Anne Porter is highly praised by many readers for her flexible artistic style,accurate and vivid description of characters,profound connotati...In the field of vision of American Literature in the 20th century,Katherine Anne Porter is highly praised by many readers for her flexible artistic style,accurate and vivid description of characters,profound connotation of her works.As a female writer,Porter is good at creating female characters from a unique female perspective,and reveals the inner activities of female characters in self-development and the female individual consciousness and independent consciousness pursued by women through her works.Although she doesn’t consciously involve feminism,we can see that the heroine’s female consciousness is gradually awakening from her portrayal of female characters and her exploration of female inner world.This paper takes Porter’s short story Theft published in the 1990s as an example to analyze the feminist consciousness in her works from the perspective of feminism.展开更多
This paper aims at analyzing the impact of the neutral conductor absence at specific sections over the performance of the power distribution lines, and proposing alternative solutions to mitigate the problems caused b...This paper aims at analyzing the impact of the neutral conductor absence at specific sections over the performance of the power distribution lines, and proposing alternative solutions to mitigate the problems caused by the neutral conductor theft. Simulations are made by the software lnterplan and show that the absence of neutral conductor at specific sections of power distribution lines may increase the neutral-to-ground voltages, which compromises the system＇s safety. The solution developed keeps the technical performance of the power distribution system at satisfactory levels, regarding the voltage profile, or, at least, close to the level before the neutral conductor＇s theft.展开更多
Smart grid systems are advancing electrical services,making them more compatible with Internet of Things(IoT)technologies.The deployment of smart grids is facing many difficulties,requiring immediate solutions to enha...Smart grid systems are advancing electrical services,making them more compatible with Internet of Things(IoT)technologies.The deployment of smart grids is facing many difficulties,requiring immediate solutions to enhance their practicality.Data privacy and security are widely discussed,and many solutions are proposed in this area.Energy theft attacks by greedy customers are another difficulty demanding immediate solutions to decrease the economic losses caused by these attacks.The tremendous amount of data generated in smart grid systems is also considered a struggle in these systems,which is commonly solved via fog computing.This work proposes an energytheft detection method for smart grid systems employed in a fog-based network infrastructure.This work also proposes and analyzes Zero-day energy theft attack detection through a multi-layered approach.The detection process occurs at fog nodes via five machine-learning classification models.The performance of the classifiers is measured,validated,and reported for all models at fog nodes,as well as the required training and testing time.Finally,the measured results are compared to when the detection process occurs at a central processing unit(cloud server)to investigate and compare the performance metrics’goodness.The results show comparable accuracy,precision,recall,and F1-measure performance.Meanwhile,the measured execution time has decreased significantly in the case of the fog-based network infrastructure.The fog-based model achieved an accuracy and recall of 98%,F1 score of 99%,and reduced detection time up to around 85%compared to the cloud-based approach.展开更多
With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft detection.However,the imbalance of electricity consumption data is vio...With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft detection.However,the imbalance of electricity consumption data is violent,which makes the training of detection model challenging.In this case,this paper proposes an electricity theft detection method based on ensemble learning and prototype learning,which has great performance on imbalanced dataset and abnormal data with different abnormal level.In this paper,convolutional neural network(CNN)and long short-term memory(LSTM)are employed to obtain abstract feature from electricity consumption data.After calculating the means of the abstract feature,the prototype per class is obtained,which is used to predict the labels of unknown samples.In the meanwhile,through training the network by different balanced subsets of training set,the prototype is representative.Compared with some mainstream methods including CNN,random forest(RF)and so on,the proposed method has been proved to effectively deal with the electricity theft detection when abnormal data only account for 2.5%and 1.25%of normal data.The results show that the proposed method outperforms other state-of-the-art methods.展开更多
Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of...Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of advanced metering infrastructure(AMI)and Smart Grid allows all participants in the distribution grid to store and track electricity consumption.During the research,a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings.This model is an ensemble meta-algorithm(stacking)that generalizes the algorithms of random forest,LightGBM,and a homogeneous ensemble of artificial neural networks.The best accuracy of the proposed meta-algorithm in comparison to basic classifiers is experimentally confirmed on the test sample.Such a model,due to good accuracy indicators(ROC-AUC-0.88),can be used as a methodological basis for a decision support system,the purpose of which is to form a sample of suspected NTL sources.The use of such a sample will allow the top management of electric distribution companies to increase the efficiency of raids by performers,making them targeted and accurate,which should contribute to the fight against NTL and the sustainable development of the electric power industry.展开更多
打孔盗油事件不但给国家造成巨大经济损失,还可能危害国家能源安全、生态安全和公共安全,对打孔盗油须防患于未然,以避免危害结果的发生。通过深入分析打孔盗油嫌疑车辆的行为特征,引入多维度增量式DBSCAN算法(increment Density-Based ...打孔盗油事件不但给国家造成巨大经济损失,还可能危害国家能源安全、生态安全和公共安全,对打孔盗油须防患于未然,以避免危害结果的发生。通过深入分析打孔盗油嫌疑车辆的行为特征,引入多维度增量式DBSCAN算法(increment Density-Based Spatial Clustering of Applications with Noise,IncDBSCAN),挖掘公安视频监控车辆抓拍数据中的潜在规律,可有效识别在管道保护区内活动的涉嫌盗油异常车辆。在与传统的支持向量机(Support Vector Machine,SVM)、基于密度带有噪声的空间聚类算法(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)、K均值聚类算法(K-Means Clustering Algorithm,K-Means)的对比试验中,多维度IncDBSCAN模型具有更好的检测效果,其精确率为85%,召回率为82%,F1值为83.4%,均优于其他模型。该方法为输油管道打孔盗油视频智能预警提供了一种新的思路和手段。展开更多
文摘Electricity theft significantly impacts the reliability and sustainability of electricity services,particularly in developing regions.However,the socio-economic,infrastructural,and institutional drivers of theft remain inadequately explored.Here we examine electricity theft in Lubumbashi,Democratic Republic of Congo,focusing on its patterns,causes,and impacts on service quality.Theft rates exceeded 75%in peripheral municipalities like Katuba and Kampemba,driven by poverty,weak law enforcement,and poor infrastructure dominated by above-ground networks.In contrast,central areas like Kamalondo and Lubumbashi reported lower theft rates due to better urban planning and underground systems.We found that electricity theft directly correlates with frequent voltage fluctuations,prolonged outages,and grid overloads.Socio-economic factors,including high connection fees and poverty,emerged as primary drivers,while institutional weaknesses such as corruption and ineffective enforcement perpetuate theft.Addressing theft requires a holistic approach integrating infrastructure modernization,socio-economic reforms,and institutional strengthening.Transitioning to underground networks,providing affordable electricity access,and adopting advanced metering systems are crucial.Overall,this study highlights the systemic nature of electricity theft and provides actionable insights for improving electricity service delivery and equity in urban settings.
文摘Generically, SCM may be said to include all activities carried out to ensure proper functioning of the supply chain. The activities included in proper management of a supply chain broadly include logistics activities, planning and control of the flow of information and materials in a firm, management of relationships with other organizations and government intervention, However, crude oil theft and pipeline vandalism are established products supply chain disruptors in Nigeria which are rendering the task of running an efficient petroleum supply chain onerous. This paper aims to establish the importance of effective supply chain strategies for companies in the oil and gas industry with special focus on the Nigerian oil and gas sector and the strategies by which the state oil and gas corporation in this sector may mitigate disruptions to its supply chain. This study investigates the enhancement of supply chain strategies towards meeting the challenge of crude oil theft and pipeline vandalism, using the Nigerian National Corporation (NNPC) as a case study. Based on this study, data were collected from two sources: A summary of incident reports obtained from NNPC and an interview with personnel in the PPMC Department. Incident report refers to a report produced when accidents such as equipment failure, injury, loss of life, or fire occur at the work site. Content analysis is utilized to evaluate data obtained from interview responses, CBN financial stability reports, NDIC annual reports, circulars, banking supervision reports and implementation guidelines. The study found out that NNPC should endeavor to sustain its value chain and ward of pipeline vandals and crude oil thieves by engaging in community partnership, detailing security outfits to ensure its pipelines’ right of way and bridging. Concluded that the oil supply chain of the Nigerian National Petroleum Corporation has been plagued by disruptions in the form of crude oil theft and pipeline vandalism which has had debilitating effects on its value.
基金supported by the National Natural Science Foundation of China(U1766210).
文摘With the application of the advanced measurement infrastructure in power grids,data driven electricity theft detection methods become the primary stream for pinpointing electricity thieves.However,owing to anomaly submergence,which shows that the usage patterns of electricity thieves may not always deviate from those of normal users,the performance of the existing usage-pattern-based method could be affected.In addition,the detection results of some unsupervised learning algorithm models are abnormal degrees rather than“0-1”to ascertain whether electricity theft has occurred.The detection with fixed threshold value may lead to deviation and would not be sufficiently flexible to handle the detection for different scenes and users.To address these issues,this study proposes a new electricity theft detection method based on load shape dictionary of users.A corresponding strategy for tunable threshold is proposed to optimize the detection effect of electricity theft,and the efficacy and applicability of the proposed adaptive electricity theft detection method were verified from numerical experiments.
文摘One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously possible.For this purpose,we have taken historical data of energy thieves and normal users.To avoid imbalance observation,biased estimates,we applied the interpolation method.Furthermore,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.By proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time complexity.To minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark scheme.Our proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,respectively.Furthermore,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.
基金supported by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sk?odowska-Curie Grant Agreement(801522)Science Foundation Ireland and co-funded by the European Regional Development Fund through the ADAPT Centre for Digital Content Technology(13/RC/2106_P2)。
文摘Electricity theft is one of the major issues in developing countries which is affecting their economy badly.Especially with the introduction of emerging technologies,this issue became more complicated.Though many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)techniques.Here,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and limitations.Three levels of taxonomy are presented to classify state-of-the-art ETD techniques.Different types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from literature.The challenges of different ETD techniques and their mitigation are suggested for future work.It is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft tracking.This can help in the prevention of energy theft,in the future,as well as for ETD.
文摘Unauthorized use of energy is the major source of the non-technical losses of the energy in developing countries. Gas theft as a kind of energy theft is an increasing issue in a number of countries particularly in developing countries. This study is an attempt to address the issue of gas theft through the deployment of Geographic Information System (GIS) capabilities (Spatial Analysis) to import external factors into the current gas theft detection methods, improve data mining processes, and offer some management solutions. To achieve the intended goals in the study, two types of data sources were collected and analyzed: internal data such as reported instances of gas theft, and some customer properties, and external data such as some demographic data. In order to analyze and modeling the gas theft and the relationships between variables we used Hotspot analysis, Ordinary Least Squares regression (OLS) and Geographically Weighted Regression (GWR) analysis with ArcGIS tools. The results from clustering test indicated that the gas theft is not a random phenomenon in all areas of Tabriz and there are underlying factors. Mapping clusters by the hotspot techniques suggested the locations of clusters and areas at risk. The results of the regression analysis illustrated the importance of external factors clearly. According to the results, we recommend a conceptual GIS framework to select high risk areas as a subset data for a meter data analysis. Results of this research are of great importance for GIS based spatial analysis and can be used as base of future researches.
文摘The article investigates the similarities and differences between all versions of Grand Theft Auto as an adventure game with the widest popularity in the last decade. The game is a story collection, a frame for performance, a virtual museum of vernacular culture and a widely circulated pop culture artifact whose double-voiced aesthetic has given rise to diverse interpretive communities. The aim of comparing the differences and similarities between different versions of the game is to be able to evaluate the game from the user’s point of view. With this aim, whether with the verisimilitude that the different versions offer makes GTA a product of an iterative design process or not will be displayed.
文摘In the field of vision of American Literature in the 20th century,Katherine Anne Porter is highly praised by many readers for her flexible artistic style,accurate and vivid description of characters,profound connotation of her works.As a female writer,Porter is good at creating female characters from a unique female perspective,and reveals the inner activities of female characters in self-development and the female individual consciousness and independent consciousness pursued by women through her works.Although she doesn’t consciously involve feminism,we can see that the heroine’s female consciousness is gradually awakening from her portrayal of female characters and her exploration of female inner world.This paper takes Porter’s short story Theft published in the 1990s as an example to analyze the feminist consciousness in her works from the perspective of feminism.
文摘This paper aims at analyzing the impact of the neutral conductor absence at specific sections over the performance of the power distribution lines, and proposing alternative solutions to mitigate the problems caused by the neutral conductor theft. Simulations are made by the software lnterplan and show that the absence of neutral conductor at specific sections of power distribution lines may increase the neutral-to-ground voltages, which compromises the system＇s safety. The solution developed keeps the technical performance of the power distribution system at satisfactory levels, regarding the voltage profile, or, at least, close to the level before the neutral conductor＇s theft.
文摘Smart grid systems are advancing electrical services,making them more compatible with Internet of Things(IoT)technologies.The deployment of smart grids is facing many difficulties,requiring immediate solutions to enhance their practicality.Data privacy and security are widely discussed,and many solutions are proposed in this area.Energy theft attacks by greedy customers are another difficulty demanding immediate solutions to decrease the economic losses caused by these attacks.The tremendous amount of data generated in smart grid systems is also considered a struggle in these systems,which is commonly solved via fog computing.This work proposes an energytheft detection method for smart grid systems employed in a fog-based network infrastructure.This work also proposes and analyzes Zero-day energy theft attack detection through a multi-layered approach.The detection process occurs at fog nodes via five machine-learning classification models.The performance of the classifiers is measured,validated,and reported for all models at fog nodes,as well as the required training and testing time.Finally,the measured results are compared to when the detection process occurs at a central processing unit(cloud server)to investigate and compare the performance metrics’goodness.The results show comparable accuracy,precision,recall,and F1-measure performance.Meanwhile,the measured execution time has decreased significantly in the case of the fog-based network infrastructure.The fog-based model achieved an accuracy and recall of 98%,F1 score of 99%,and reduced detection time up to around 85%compared to the cloud-based approach.
基金supported by National Natural Science Foundation of China(No.52277083).
文摘With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft detection.However,the imbalance of electricity consumption data is violent,which makes the training of detection model challenging.In this case,this paper proposes an electricity theft detection method based on ensemble learning and prototype learning,which has great performance on imbalanced dataset and abnormal data with different abnormal level.In this paper,convolutional neural network(CNN)and long short-term memory(LSTM)are employed to obtain abstract feature from electricity consumption data.After calculating the means of the abstract feature,the prototype per class is obtained,which is used to predict the labels of unknown samples.In the meanwhile,through training the network by different balanced subsets of training set,the prototype is representative.Compared with some mainstream methods including CNN,random forest(RF)and so on,the proposed method has been proved to effectively deal with the electricity theft detection when abnormal data only account for 2.5%and 1.25%of normal data.The results show that the proposed method outperforms other state-of-the-art methods.
文摘Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of advanced metering infrastructure(AMI)and Smart Grid allows all participants in the distribution grid to store and track electricity consumption.During the research,a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings.This model is an ensemble meta-algorithm(stacking)that generalizes the algorithms of random forest,LightGBM,and a homogeneous ensemble of artificial neural networks.The best accuracy of the proposed meta-algorithm in comparison to basic classifiers is experimentally confirmed on the test sample.Such a model,due to good accuracy indicators(ROC-AUC-0.88),can be used as a methodological basis for a decision support system,the purpose of which is to form a sample of suspected NTL sources.The use of such a sample will allow the top management of electric distribution companies to increase the efficiency of raids by performers,making them targeted and accurate,which should contribute to the fight against NTL and the sustainable development of the electric power industry.
文摘打孔盗油事件不但给国家造成巨大经济损失,还可能危害国家能源安全、生态安全和公共安全,对打孔盗油须防患于未然,以避免危害结果的发生。通过深入分析打孔盗油嫌疑车辆的行为特征,引入多维度增量式DBSCAN算法(increment Density-Based Spatial Clustering of Applications with Noise,IncDBSCAN),挖掘公安视频监控车辆抓拍数据中的潜在规律,可有效识别在管道保护区内活动的涉嫌盗油异常车辆。在与传统的支持向量机(Support Vector Machine,SVM)、基于密度带有噪声的空间聚类算法(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)、K均值聚类算法(K-Means Clustering Algorithm,K-Means)的对比试验中,多维度IncDBSCAN模型具有更好的检测效果,其精确率为85%,召回率为82%,F1值为83.4%,均优于其他模型。该方法为输油管道打孔盗油视频智能预警提供了一种新的思路和手段。
