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Inspired by the powerful feature extraction and the data reconstruction ability of autoencoder, a stacked sparse denoising autoencoder is developed for electricity theft detection in this paper. The technical route is to employ the electricity data from honest users as the training samples, and the autoencoder can learn the effective features from the data and then reconstruct the inputs as much as possible. For the anomalous behavior, since it contributes little to the autoencoder, the detector returns to a comparatively higher reconstruction error; hence the theft users can be recognized by setting an appropriate error threshold. To improve the feature extraction ability and the robustness, the sparsity and noise are introduced into the autoencoder, and the particle swarm optimization algorithm is applied to optimize these hyper-parameters. Moreover, the receiver operating characteristic curve is put forward to estimate the optimal error threshold. Finally, the proposed approach is evaluated and verified using the electricity dataset in Fujian, China. [ABSTRACT FROM AUTHOR]