The performance of existing consumption-pattern based models is not sufficiently satisfactory for application. This is partly because most of them have focused on the selection of classification algorithms rather than the design of features

and existing feature design methods with respect to electricity nontechnical losses(NTL)detection remain inefficient and unsatisfactory. A deep-learning based feature extraction model is proposed for NTL detection

namely the stacked uncorrelating autoencoder(SUAE). Due to the deep architecture and powerful uncorrelating ability of SUAE

features are extracted from load profiles concisely and effectively

which has thus enabled a great improvement in final NTL detection performance. Support vector machines(SVM)are applied as classifiers

which use the features extracted by SUAE to output a judgment result. Case studies on real datasets have demonstrated that the proposed NTL model has high relevance ratio and low false alarm rate

as well as the powerful feature extraction ability of the SUAE.