Journal ArticleDOI
Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids
TLDR
A novel electricity-theft detection method based on wide and deep convolutional neural networks (CNN) model that outperforms other existing methods in detection accuracy and captures the global features of 1-D electricity consumption data.Abstract:
Electricity theft is harmful to power grids. Integrating information flows with energy flows, smart grids can help to solve the problem of electricity theft owning to the availability of massive data generated from smart grids. The data analysis on the data of smart grids is helpful in detecting electricity theft because of the abnormal electricity consumption pattern of energy thieves. However, the existing methods have poor detection accuracy of electricity theft since most of them were conducted on one-dimensional (1-D) electricity consumption data and failed to capture the periodicity of electricity consumption. In this paper, we originally propose a novel electricity-theft detection method based on wide and deep convolutional neural networks (CNN) model to address the above concerns. In particular, wide and deep CNN model consists of two components: the wide component and the deep CNN component. The deep CNN component can accurately identify the nonperiodicity of electricity theft and the periodicity of normal electricity usage based on 2-D electricity consumption data. Meanwhile, the wide component can capture the global features of 1-D electricity consumption data. As a result, wide and deep CNN model can achieve the excellent performance in electricity-theft detection. Extensive experiments based on realistic dataset show that wide and deep CNN model outperforms other existing methods.read more
Citations
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Journal ArticleDOI
Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach
TL;DR: An electricity theft detection system is proposed based on a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) architecture that can classify both the majority class and the minority class with good accuracy.
Journal ArticleDOI
Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives
TL;DR: An in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence is presented, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted.
Journal ArticleDOI
Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies
TL;DR: The enabling technologies of big data analytics of manufacturing data are surveyed and discussed and the future directions in this promising area are outlined.
Journal ArticleDOI
Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities
Tanveer Ahmad,Tanveer Ahmad,Dongdong Zhang,Chao Huang,Chao Huang,Hongcai Zhang,Ning-Yi Dai,Yonghua Song,Yonghua Song,Huanxin Chen +9 more
TL;DR: How AI techniques outperform traditional models in controllability, big data handling, cyberattack prevention, smart grid, IoT, robotics, energy efficiency optimization, predictive maintenance control, and computational efficiency is explored.
Journal ArticleDOI
A Novel Combined Data-Driven Approach for Electricity Theft Detection
TL;DR: The maximum information coefficient (MIC) can be used to precisely detect thefts that appear normal in shapes and the clustering technique by fast search and find of density peaks (CFSFDP) finds the abnormal users among thousands of load profiles, making it quite suitable for detecting electricity thefts with arbitrary shapes.
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Posted Content
Multi-Scale Context Aggregation by Dilated Convolutions
Fisher Yu,Vladlen Koltun +1 more
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