The　recovery　of　stolen　electricity　is　the　ultimate　goal　of　electricity　theft　detection,　and　accurate　identification　of　electricity　theft　time　is　an　important　basis　for　accurately　estimating　stolen　electricity.　However,　existing　electricity　theft　detection　methods　focus　on　identifying　electricity　theft　behavior　and　need　more　in-depth　analysis　of　electricity　theft　time.　There　is　an　urgent　need　to　study　electricity　theft　time　identification　models　based　on　electricity　thieves＇　metering　data　to　provide　a　basis　for　estimating　electricity　theft.　Aiming　to　address　the　problem　of　electricity　theft　time　identification,　a　semi-supervised　electricity　theft　data　classification　method　based　on　a　transformer　and　one-class　support　vector　machine　(OCSVM)　is　proposed.　Firstly,　the　user　load　data　is　divided　by　day,　and　the　problem　of　identifying　the　time　of　electricity　theft　is　transformed　into　the　problem　of　power　theft　daily　load　data　discrimination.　Secondly,　the　Transformer　is　used　as　the　reconstruction　model　to　learn　the　user’s　normal　power　consumption　patterns　and　regularity　to　reconstruct　the　reconstructed　value　based　on　the　user＇s　daily　load　data.　Finally,　the　reconstructed　error　curve　is　used　as　the　input　of　the　OCSVM　to　construct　the　decision　boundary　of　normal　power　consumption　behavior.　Then　the　electricity　theft　data　is　identified　to　realize　the　electricity　theft　time　identification.　An　example　analysis　was　carried　out　based　on　smart　meter　user　data　in　a　southern　province　to　verify　the　feasibility　and　effectiveness　of　this　method.　The　experimental　results　show　that　this　method　has　good　sensitivity　and　robustness.　©　2025　Power　System　Technology　Press.　All　rights　reserved.