Non‐technical loss (NTL) is detrimental to the smart grid. Intelligent application of advanced metering infrastructure (AMI) helps to solve NTL detection and classification. By using advanced learning algorithms, data analysis on the massive data generated by AMI is helpful in the detection and classification of electricity theft. Conventional data analysis algorithm, like Support Vector Machine (SVM), Random Forest Algorithm (RFA), and 1D‐ Conventional Neural Network (1D‐CNN), has low detection and classification accuracy of electricity theft. Because these methods failed to predict and classify multidimensional electricity consumption data by various consumers in AMI in the smart grid. In this research work, a multidimensional deep learning algorithm is proposed to learn and classify the non‐periodicity of electricity. This helps to detect electricity theft by a consumer from the periodic load curve. Both weekly load patterns and daily load patterns are processed as 2D electricity data samples. From the proposed multidimensional deep learning model, an average classification accuracy of 97.5% and a precision‐recall of 0.97 were obtained. This validates that the proposed deep learning model outperforms other conventional data analysis classification algorithm.

中文翻译：

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通过使用深度学习算法对非技术损失进行检测和分类来增强智能电网安全性

非技术损失（NTL）对智能电网有害。先进的计量基础结构（AMI）的智能应用有助于解决NTL检测和分类。通过使用高级学习算法，对AMI生成的海量数据进行数据分析有助于检测和分类电盗窃行为。诸如支持向量机（SVM），随机森林算法（RFA）和一维-常规神经网络（1D-CNN）之类的常规数据分析算法对电盗窃的检测和分类准确性较低。因为这些方法无法预测智能电网中AMI中各种用户的多维用电量数据并对其进行分类。在这项研究工作中，提出了一种多维深度学习算法来学习和分类电的非周期性。这有助于从周期性负载曲线中检测出用户盗窃电。每周负​​荷模式和每日负荷模式均作为2D电力数据样本进行处理。从提出的多维深度学习模型中，平均分类精度为97.5％，精确召回率为0.97。这验证了所提出的深度学习模型优于其他常规数据分析分类算法。