Abnormal Electricity Consumption Research Articles

A Monitoring Model for Abnormal Electricity Consumption Based on K-Means++ Clustering and Improved K-Nearest Neighbor Algorithm

A Monitoring Model for Abnormal Electricity Consumption Based on K-Means++ Clustering and Improved K-Nearest Neighbor Algorithm

Review of Detection Methods for Abnormal Electricity Consumption Data in Smart Grid

The smart grid is an intelligent system of the power grid, which is a communication information support platform based on the coordinated development of the transmission network and various levels of power grids. It is a highly integrated system characterized by informationization, automation, and interactivity of various voltage levels, including transmission and transformation, distribution, and power dispatch. This article summarizes, analyzes, and summarizes the methods for detecting abnormal electricity consumption data in smart grids. It introduces the detection methods for abnormal electricity consumption data based on traditional technology and artificial intelligence technology, analyzes and elaborates on the basic principles and characteristics of each method, summarizes and looks forward to the challenges and future development trends faced by abnormal electricity consumption data detection in smart grids, and provides some reference for subsequent research.

Anomaly Detection Model of Time Segment Power Usage Behavior Using Unsupervised Learning

<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 electricity usage within specific time segments, this research employs an unsupervised machine learning method, developing an anomaly detection model for the overall electricity consumption behavior of residential users. The model focuses on analyzing 2-hour intervals of electricity consumption, enabling more effective detection of abnormal usage patterns. It is trained using power consumption data collected from five actual residential users as part of an experimental study. The results indicate that the proposed anomaly detection model achieves performance metrics such as Precision, Recall, and F1-score of 0.90 or above, showcasing its potential for practical implementation.</p> <p> </p>

An anomaly Detection Method for Electricity Consumption Data Based on CNN-BiLSTM-Attention

As the complexity and uncertainty of smart distribution networks increase, data security issues in smart meters have become a pressing challenge, such as false data injection and electricity theft. To ensure fairness, safety, and overall economic efficiency of distribution networks, it is essential to accurately detect abnormal electricity consumption. However, traditional methods relying on on-site inspections by grid personnel suffer from low efficiency and high costs in detecting user anomalies. This paper proposes an electricity consumption data anomaly detection method based on CNN-BiLSTM-Attention. CNN is utilized to extract data features, while BiLSTM and attention mechanisms capture contextual information in sequence data. Furthermore, experiments conducted on data extracted from smart meters demonstrate that the proposed model outperforms other models in anomaly detection, with accuracy, recall, and F1-Score all exceeding 91%. These results validate the effectiveness and feasibility of the proposed method, providing an efficient solution for user anomaly detection in national power grids.

Gated Recurrent Units Based Abnormal Detection Method for Imbalanced Electricity Consumption Data

Timely detection of abnormal electricity consumption behaviors plays an important role in saving energy. But the detection of abnormal electricity consumption is faced with many problems. Imbalanced data are important challenges in abnormal electricity detection. When normal data are much more than abnormal data, the model can hardly learn the features of the minority class data, which leads to low detection efficiency. Therefore, in this paper, we employ adaptive synthetic sampling (ADASYN) to achieve effective expansion of the minority class data. In addition, we adopt gated recurrent units to realize the classification of electricity consumption data.We conduct detailed experiments to verify our proposed method. Experimental results show that our method is more effective than other methods.

Abnormal Electricity Consumption Detection Method for Smart Grid Using Fusion Matrix Completion and Improved Clustering Algorithm

Abnormal Electricity Consumption Detection Method for Smart Grid Using Fusion Matrix Completion and Improved Clustering Algorithm

A Power Load Forecasting Method Based on Intelligent Data Analysis

Abnormal electricity consumption behavior not only affects the safety of power supply but also damages the infrastructure of the power system, posing a threat to the secure and stable operation of the grid. Predicting future electricity consumption plays a crucial role in resource management in the energy sector. Analyzing historical electricity consumption data is essential for improving the energy service capabilities of end-users. To forecast user energy consumption, this paper proposes a method that combines adaptive noise-assisted complete ensemble empirical mode decomposition (CEEMDAN) with long short-term memory (LSTM) networks. Firstly, considering the challenge of directly applying prediction models to non-stationary and nonlinear user electricity consumption data, the adaptive noise-assisted complete ensemble empirical mode decomposition algorithm is used to decompose the signal into trend components, periodic components, and random components. Then, based on the CEEMDAN decomposition, an LSTM prediction sub-model is constructed to forecast the overall electricity consumption by using an overlaying approach. Finally, through multiple comparative experiments, the effectiveness of the CEEMDAN-LSTM method is demonstrated, showing its ability to explore hidden temporal relationships and achieve smaller prediction errors.

Transfer learning based graph convolutional network with self-attention mechanism for abnormal electricity consumption detection

In the case of limited energy, the waste of energy and economic loss caused by abnormal electricity consumption should not be underestimated and its detection plays an important role. However, abnormal electricity consumption detection also faces many challenges. On the one hand, labeled abnormal data are difficult to obtain. On the other hand, building different models for different users undoubtedly increases the demand for data and the burden of training. To tackle these challenges, in this paper, we propose a transfer learning based graph convolutional network with self-attention mechanism method to detect abnormal electricity consumption. With the help of transfer learning, we firstly pre-train the source domain network using the sufficient data. Then, a small amount of data in the target domain is utilized to fine-tune the pre-training model to get the final detection model. This can not only effectively alleviate the problem of insufficient data, but also reduce the training burden caused by building different models for different users. In addition, to improve the effect of feature extraction and enhance the performance of the network, we employ the self-attention mechanism to enhance the network’s attention to different data information. Finally, we adopt the graph convolutional networks to discover the relationships of electricity consumption data among different moments and to classify the electricity consumption data. We have done detailed experiments to verify the effectiveness of the proposed method, and experimental results show that the proposed method is effective and robust.

P2Detect: Electricity Theft Detection With Privacy Preservation for Both Data and Model in Smart Grid

Electricity Theft Detection (ETD) based on deep learning can detect abnormal electricity consumption behaviors by analyzing user historical data. However, existing ETD schemes conduct neural network inferencing in plaintext without strong privacy guarantees, moreover, none has considered the privacy of proprietary models from electric utilities. To address those issues, we introduce p2Detect, a privacy-preserving ETD scheme via neural network inferencing over encrypted data. p2Detect works in a two-party setting where model is held by Detection Server (DS) and data are held by Edge Server (ES) on behalf of multiple Smart Meters (SM). First, we design a homomorphic-encryption-friendly model and its corresponding training methods based on region search and clustering; Second, our customized cryptographic protocols enable privacy-preserving yet efficient ETD inferencing over encrypted data, without revealing private inputs to DS or revealing model parameters to ES. The performance evaluations demonstrate: (i) Model optimizations achieve AUC as high as 0.763, where AUC is a robust evaluation criterion for inferencing ability of the model on unbalanced dataset. (ii) p2Detect takes 73.3ms to execute ETD per SM on average. (iii) Communication cost between DS and ES is 196.76KB per SM. Therefore, p2Detect preserves strong privacy while maintaining high accuracy and efficiency.

The Empty-Nest Power User Management Based on Data Mining Technology.

With the aging of the social population structure, the number of empty-nesters is also increasing. Therefore, it is necessary to manage empty-nesters with data mining technology. This paper proposed an empty-nest power user identification and power consumption management method based on data mining. Firstly, an empty-nest user identification algorithm based on weighted random forest was proposed. Compared with similar algorithms, the results indicate that the performance of the algorithm is the best, and the identification accuracy of empty-nest users is 74.2%. Then a method for analyzing the electricity consumption behavior of empty-nest users based on fusion clustering index adaptive cosine K-means was proposed, which can adaptively select the optimal number of clusters. Compared with similar algorithms, the algorithm has the shortest running time, the smallest Sum of the Squared Error (SSE), and the largest mean distance between clusters (MDC), which are 3.4281 s, 31.6591 and 13.9513, respectively. Finally, an anomaly detection model with an Auto-regressive Integrated Moving Average (ARIMA) algorithm and an isolated forest algorithm was established. The case analysis shows that the recognition accuracy of abnormal electricity consumption for empty-nest users was 86%. The results indicate that the model can effectively detect the abnormal behavior of empty-nest power users and help the power department to better serve empty-nest users.

Empirical mode decomposition-based multi-scale spectral graph convolution network for abnormal electricity consumption detection

Empirical mode decomposition-based multi-scale spectral graph convolution network for abnormal electricity consumption detection

A Novel Combined DenseNet and Gated Recurrent Unit Approach to Detect Energy Thefts in Smart Grids

Due to the illegal use of electricity, non-technical losses are exponentially increasing in electricity distribution systems day by day. With the debut of smart meters in the smart grid, new electricity theft attacks are welcomed. The investigation of abnormal electricity consumption patterns helps in detecting electricity thieves. Moreover, existing methods have poor electricity theft detection (ETD) accuracy due to imbalanced datasets provided by the utilities. They have also failed to capture both periodicity and non-periodicity of 1-D daily electricity usage data. We primarily propose a novel sampling technique to balance the dataset, named as random oversampling using both classes (ROBC). This technique performs oversampling using both the theft and normal classes. With this technique, the problem of low accuracy has been resolved. We also propose a unique ETD model using densenet-fully convolutional network (DenseNet-FCN) and gated recurrent unit (GRU) with a light gradient boosting machine (LightGBM), known as DenseNet-GRU-LightGBM, to address the above mentioned concerns. DenseNet-FCN module extracts periodic and non-periodic patterns from 2-D electricity consumption data in a precise way. Whereas, GRU module captures as well as memorizes features from 1-D consumption data. Afterwards, LightGBM module is used as an ensemble classifier to give final ETD results. As a result, the proposed model has excellent ETD results. Comprehensive simulations indicate that the proposed scheme outperforms other existing methods regarding ETD.

Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid

In the smart grid (SG), user consumption data are increasing very rapidly. Some users consume electricity legally, while others steal it. Electricity theft causes significant damage to power grids, affects power supply efficiency, and reduces utility revenues. This study helps utilities reduce the problems of electricity theft, inefficient electricity monitoring, and abnormal electricity consumption in smart grids. To this end, an electricity theft dataset from the state grid corporation of China (SGCC) is employed and this study develops a novel model, a mixture of convolutional neural network and gated recurrent unit (CNN-GRU), for automatic power theft detection. Moreover, the hyperparameters of the proposed model are tuned using a meta-heuristic method, the cuckoo search (CS) algorithm. The class imbalance problem is solved using the synthetic minority oversampling technique (SMOTE). The clean data are trained and then tested with the proposed classification. Extensive simulations are performed based on real energy consumption data. The simulated results show that the proposed theft detection model (CNN-GRU-CS) solved the theft classification problem better than other approaches in terms of effectiveness and accuracy by 10% on average. The calculated accuracy of the proposed method is 92% and the precision is 94%.

GoogLeResNet3 network for detecting the abnormal electricity consumption behavior of users

With users’ increasing knowledge and intellectualization, users’ abnormal electricity consumption behaviors (AECB) are becoming more prevalent. Since access to renewable energy sources leads to a volatile and intermittent electricity load, the existing artificial intelligence methods are challenging to detect the AECB of users. To quickly and accurately identify the AECB of a massive number of users, this work proposes the GoogLeResNet3 network module, which contains fully connected layers, the Inception module, and a residual module. The GoogLeResNet3 network is compared with the GoogLeNet module, ResNet-50, ResNet-101, and 11 other neural networks. The results of the comparison experiments indicate that: the GoogLeResNet3 network with the highest accuracy is 335 s quicker than the second-fast network, and the accuracy is 10.57 % higher than the second-best network at least.

Anomaly electricity detection method based on entropy weight method and isolated forest algorithm

This study aims at investigating the applicability of abnormal electricity consumption data detection method, which is based on the entropy weight method and the isolated forest tree algorithm. The inaccessibility and imbalance of abnormal electricity consumption samples in actual data sets are considered by analyzing smart distribution network power consumption big data. Firstly, the users are classified by the k-means clustering algorithm, and then the characteristics of each type of user are extracted and the feature set is processed by the principal component analysis method to reduce the dimensions, followed by the entropy weight method adaptive configuration of the weight coefficients of each feature index, and finally the abnormal power consumption users are calculated based on the feature-weighted isolated forest algorithm. The algorithm verifies the real electricity consumption data of 6,445 users, and the results show that the method has a high detection accuracy, recall rate and F1 score, which is more suitable for the detection of abnormal electricity consumption in scenarios when there are complex and diverse user power consumption behaviors.

Feature Extraction of Anomaly Electricity Usage Behavior in Residence Using Autoencoder

Due to the climate crisis, energy-saving issues and carbon reduction have become the top priority for all countries. Owing to the increasing popularity of advanced metering infrastructure and smart meters, the cost of acquiring data on residential electricity consumption has substantially dropped. This change promotes the analysis of residential electricity consumption, which features both small and complicated consumption behaviors, using machine learning to become an important research topic among various energy saving and carbon reduction measures. The main subtopic of this subject is the identification of abnormal electricity consumption behaviors. At present, anomaly detection is typically realized using models based on low-level features directly collected by sensors and electricity meters. However, due to the significant number of dimensions and a large amount of redundant information in these low-level features, the training efficiency of the model is often low. To overcome this, this study adopts an autoencoder, which is a deep learning technology, to extract the high-level electricity consumption information of residential users to improve the anomaly detection performance of the model. Subsequently, this study trains one-class SVM models for anomaly detection by using the high-level features of five actual residential users to verify the benefits of high-level features.

Analysis of electricity stealing based on user electricity characteristics of electricity information collection system

In order to effectively combat electricity theft and improve the ability to recognize electricity theft, this paper proposes an anti-electricity theft method based on the analysis of users’ electricity consumption behavior. First, determine the appropriate outlier detection electrical parameters through reasonable research on users. , Using the outlier detection algorithm to filter the user’s electrical data, preprocessing the original data, performing multi-feature fusion analysis on the user’s electrical parameters, using the discriminant rule to filter out the outliers, and then using the electricity based on the clustering algorithm Pattern analysis for detection, to achieve active identification of low-voltage power theft. In this paper, combined with the user profile data of the electricity consumption information collection system, it conducts in-depth analysis of the user’s electricity consumption data. Through the discovery of abnormal electricity consumption data users, the analysis finds out the electricity theft users and improves the quality and efficiency of the anti-stealing work.

Industrial artificial intelligence based energy management system: Integrated framework for electricity load forecasting and fault prediction

Forecasting accuracy electricity load can help industrial enterprises optimise production scheduling based on peak and off-peak electricity prices. The electricity load forecasting results can be provided to an electricity system to improve electricity generation efficiency and minimize energy consumption by developing electricity generation plans in advance and by avoiding over or under the generation of electricity. However, because of the different informatization levels in different industries, few reliable intelligent electricity management systems are applied on the power supply side. Based on industrial big data and machine learning algorithms, this study proposes an integrated model to forecast short-term electricity load. The hybrid model based on the hybrid mode decomposition algorithms is proposed to decompose the total electricity load signal. To improve the generalisation ability of the forecasting model, a dynamic forecasting model is proposed based on the improved hybrid intelligent algorithm to forecast the short-term electricity load. The results show that the accuracy of the proposed dynamic integrated electricity load forecasting model is as high as 99%. The integrated framework could forecast abnormal electricity consumption in time and provide reliable evidence for production process scheduling.

Transfer Learning Based Graph Convolutional Network with Self-Attention Mechanism for Abnormal Electricity Consumption Detection

Transfer Learning Based Graph Convolutional Network with Self-Attention Mechanism for Abnormal Electricity Consumption Detection

Symposium Title: Cognitive Resources in Affective States and After Brain Damage

The conditions for economic motivation for cost-effective use of resources are a prime requirement if an organization needs to implement energy-saving technologies, regardless of its purpose and type of activity. Energy-saving methods must be applied both at the technical level, and the system (management) level. The basis of energy saving is the systematic implementation of a set of technical and technological measures, which should be preceded by the optimization of organizations’ energy consumption at the system level. The chapter uses a rank analysis to show the efficiency of approximating the electricity consumption at the units of mining enterprises. The interval estimation procedure allowed for the identification and grading the mining enterprise units, revealing those with the abnormal electricity consumption. The trend of time series of relative electricity consumption for each facility is calculated. The chapter uses the results of calculations containing the health index figures for electricity consumption to establish facilities that require a priority in-depth energy inspection. This chapter focuses on the development of a methodology for timely identification of priority units for an in-depth energy survey for the implementation of energy saving projects and increasing the efficiency of electricity use in mining enterprises as objects of technocenosis.