In this work we propose a novel self-attention mechanism model to address electricity theft detection on an imbalanced realistic dataset that presents a daily electricity consumption provided by State Grid Corporation of China. Our key contribution is the introduction of a multi-head self-attention mechanism concatenated with dilated convolutions and unified by a convolution of kernel size $1$. Moreover, we introduce a binary input channel (Binary Mask) to identify the position of the missing values, allowing the network to learn how to deal with these values. Our model achieves an AUC of $0.926$ which is an improvement in more than $17\%$ with respect to previous baseline work. The code is available on GitHub at https://github.com/neuralmind-ai/electricity-theft-detection-with-self-attention.
Tennessee Tech. University（田纳西理工大学）；Texas A&M University at Qatar（德州农工大学卡塔尔分校）
Modern smart grids rely on advanced metering infrastructure (AMI) networks for monitoring and billing purposes. However, such an approach suffers from electricity theft cyberattacks. Different from the existing research that utilizes shallow, static, and customer-specific-based electricity theft detectors, this paper proposes a generalized deep recurrent neural network (RNN)-based electricity theft detector that can effectively thwart these cyberattacks. The proposed model exploits the time series nature of the customers' electricity consumption to implement a gated recurrent unit (GRU)-RNN, h…
Non-technical losses (NTL) in electric power grids arise through electricity theft, broken electric meters or billing errors. They can harm the power supplier as well as the whole economy of a country through losses of up to 40% of the total power distribution. For NTL detection, researchers use artificial intelligence to analyse data. This work is about improving the extraction of more meaningful features from a data set. With these features, the prediction quality will increase.
Pune Institute of Computer Technology（浦那计算机技术学院）；College of Engineering, Guindy, Anna University（安娜大学金底工程学院）；Robert Bosch Centre for Data Science & Artificial Intelligence, IIT Madras（罗伯特博世数据科学与人工智能中心，马德拉斯印度理工学院）；Department of Electrical Engineering, IIT Bombay（印度理工学院孟买分校电气工程系）
Artificial intelligence-based techniques applied to the electricity consumption data generated from the smart grid prove to be an effective solution in reducing Non Technical Loses (NTLs), thereby ensures safety, reliability, and security of the smart energy systems. However, imbalanced data, consecutive missing values, large training times, and complex architectures hinder the real time application of electricity theft detection models. In this paper, we present EnsembleNTLDetect, a robust and scalable electricity theft detection framework that employs a set of efficient data pre-processing t…
Smart grids extremely rely on Information and Communications Technology (ICT) and smart meters to control and manage numerous parameters of the network. However, using these infrastructures make smart grids more vulnerable to cyber threats especially electricity theft. Electricity Theft Detection (EDT) algorithms are typically used for such purpose since this Non-Technical Loss (NTL) may lead to significant challenges in the power system. In this paper, an Ensemble Deep Convolutional Neural Network (EDCNN) algorithm for ETD in smart grids has been proposed. As the first layer of the model, a r…
Spatio-Temporal Grid Intelligence: A Hybrid Graph Neural Network and LSTM Framework for Robust Electricity Theft Detection
Department Computer Science, Abiola Ajimobi Technical University（计算机科学系，阿比奥米博技术大学）；Department of Chemical and Petroleum Engineering, Abiola Ajimobi Technical University（化学与石油工程系，阿比奥米博技术大学）
Towards Intelligent Energy Security: A Unified Spatio-Temporal and Graph Learning Framework for Scalable Electricity Theft Detection in Smart Grids
Department of Chemical and Petroleum Engineering, Abiola Ajimobi Technical University（阿比奥拉·阿吉莫比技术大学化学与石油工程系）；Department of Computer Science, Abiola Ajimobi Technical University（阿比奥拉·阿吉莫比技术大学计算机科学系）
In electricity markets, locational marginal price (LMP) forecasting is particularly important for market participants in making reasonable bidding strategies, managing potential trading risks, and supporting efficient system planning and operation. Unlike existing methods that only consider LMPs' temporal features, this paper tailors a spectral graph convolutional network (GCN) to greatly improve the accuracy of short-term LMP forecasting. A three-branch network structure is then designed to match the structure of LMPs' compositions. Such kind of network can extract the spatial-temporal featur…
Interpretable Kolmogorov-Arnold Network with Feature-Isolated Temporal Attention Mechanism for Electricity Load Forecasting
Department of Data Science and AI, Faculty of Information Technology, Monash University（蒙纳士大学信息技术学院数据科学与人工智能系）；Monash Energy Institute, Monash University（蒙纳士大学蒙纳士能源研究所）
A Hybrid Autoencoder-Transformer Model for Robust Day-Ahead Electricity Price Forecasting under Extreme Conditions
Beijing Normal-Hong Kong Baptist University（北京师范大学-香港 Baptist大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；School of Science and Engineering（科学与工程学院）；School of Electrical and Power Engineering（电气与电力工程学院）
Rising penetration levels of (residential) photovoltaic (PV) power as distributed energy resource pose a number of challenges to the electricity infrastructure. High quality, general tools to provide accurate forecasts of power production are urgently needed. In this article, we propose a supervised deep learning model for end-to-end forecasting of PV power production. The proposed model is based on two seminal concepts that led to significant performance improvements of deep learning approaches in other sequence-related fields, but not yet in the area of time series prediction: the sequence t…
Smart Energy Guardian: A Hybrid Deep Learning Model for Detecting Fraudulent PV Generation
University of Electronic Science and Technology of China（电子科技大学）；Monash University（墨尔本大学）；Shenzhen Institute for Advanced Study（深圳先进研究所）；Monash Energy Institute（墨尔本能源研究所）
The University of Tokyo（东京大学）；RIKEN AIP（理化学研究所先进智能项目中心）
Time-series data analysis is important because numerous real-world tasks such as forecasting weather, electricity consumption, and stock market involve predicting data that vary over time. Time-series data are generally recorded over a long period of observation with long sequences owing to their periodic characteristics and long-range dependencies over time. Thus, capturing long-range dependency is an important factor in time-series data forecasting. To solve these problems, we proposed two novel modules, Grouped Self-Attention (GSA) and Compressed Cross-Attention (CCA). With both modules, we…
Explainable Dual-Attention Tabular Transformer for Soil Electrical Resistivity Prediction: A Decision Support Framework for High-Voltage Substation Construction
King Mongkut’s University of technology Thonburi（泰国国王科技大学）
本研究提出一种用于预测土壤电阻率的双注意力 Transformer 架构，结合粒子群优化调参，在保持高预测精度的同时通过 SHAP 值提供可解释性，并开发了辅助高压变电站建设的 Web 决策支持框架。
KANGURA: Kolmogorov-Arnold Network-Based Geometry-Aware Learning with Unified Representation Attention for 3D Modeling of Complex Structures
Department of Electrical Engineering, Iran University of Science and Technology（伊朗科学技术大学电气工程系）；Department of Computer Engineering, Iran University of Science and Technology（伊朗科学技术大学计算机工程系）；Department of Industrial & Management Systems Engineering, West Virginia University（西弗吉尼亚大学工业与管理系统工程系）；H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology（佐治亚理工学院H.米尔顿·斯图尔特工业与系统工程学院）；Department of Computer Science, University of California, Davis（加州大学戴维斯分校计算机科学系）
Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg（卢森堡大学安全、可靠性与信任跨学科中心）；CHOICE Technologies Holding Sàrl
Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate prediction of NTLs for customers using machine learning is therefore crucial. To date, related research largely ignore that the two classes of regular and non-regular customers are highly imbalanced, that NTL proportions may change and mostly consider small data sets, often not allowing to deploy the results in production. In this paper, we present a comprehe…
Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg（卢森堡大学安全、可靠性与信任跨学科中心）；University of Quebec in Montreal（魁北克大学蒙特利尔分校）；CHOICE Technologies Holding Sàrl
Non-technical losses (NTL) occur during the distribution of electricity in power grids and include, but are not limited to, electricity theft and faulty meters. In emerging countries, they may range up to 40% of the total electricity distributed. In order to detect NTLs, machine learning methods are used that learn irregular consumption patterns from customer data and inspection results. The Big Data paradigm followed in modern machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. However, the…
Forecasting multivariate time series data, such as prediction of electricity consumption, solar power production, and polyphonic piano pieces, has numerous valuable applications. However, complex and non-linear interdependencies between time steps and series complicate the task. To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved to some good extent by recurrent neural network (RNN) with attention mechanism. Typical attention mechanism reviews the information at each previous time step and selects the relevant information to hel…
Self-Gating Attention for Efficient Time Series Forecasting
School of Automation, Southeast University（自动化学院，东南大学）；School of Electrical Engineering and Computer Science, The University of Queensland（电气工程与计算机科学学院，昆士兰大学）
