Non-technical losses (NTL) in electricity utilities are responsible for major revenue losses. In this paper, we propose a novel end-to-end solution to self-learn the features for detecting anomalies and frauds in smart meters using a hybrid deep neural network. The network is fed with simple raw data, removing the need of handcrafted feature engineering. The proposed architecture consists of a long short-term memory network and a multi-layer perceptrons network. The first network analyses the raw daily energy consumption history whilst the second one integrates non-sequential data such as its contracted power or geographical information. The results show that the hybrid neural network significantly outperforms state-of-the-art classifiers as well as previous deep learning models used in NTL detection. The model has been trained and tested with real smart meter data of Endesa, the largest electricity utility in Spain.

中文翻译：

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用于检测电力智能电表中的非技术损失的混合深度神经网络

电力公司的非技术损失 (NTL) 是造成主要收入损失的原因。在本文中，我们提出了一种新颖的端到端解决方案，以使用混合深度神经网络自学习用于检测智能电表中的异常和欺诈的特征。该网络采用简单的原始数据，无需手工制作特征工程。所提出的架构由一个长短期记忆网络和一个多层感知器网络组成。第一个网络分析原始的每日能源消耗历史，而第二个网络则整合非序列数据，如合同电力或地理信息。结果表明，混合神经网络明显优于最先进的分类器以及以前用于 NTL 检测的深度学习模型。