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.　©　2020　Elsevier　Ltd