In　order　to　solve　the　problems　of　low　precision,　heavy　workload　and　low　efficiency　in　the　traditional　sampling　method　of　the　smart　meter　error　monitoring,　a　new　method　for　error　estimation　of　electricity　meters　is　proposed.　First　of　all,　the　collected　daily　electricity　data　of　smart　meters　in　the　distribution　platform　area　are　screened　to　identify　the　light　(empty)　load　data.　Then,　the　improved　dynamic　line　loss　estimation　algorithm　is　used　to　judge　whether　there　is　a　sudden　out-of-tolerance　meter　in　the　platform　area.　Next,　the　iterative　algorithm　is　used　to　reduce　the　amount　of　suspicious　meters.　Finally,　the　clustering　algorithm　and　the　correlation　coefficient　are　used　to　locate　the　sudden　out-of-tolerance　meter,　setting　the　daily　average　error　electric　quantity　excess　threshold　to　locate　the　slowly　out-of-tolerance　electric　meter.　The　effectiveness　of　the　proposed　method　in　the　detections　of　slow　and　sudden　out-of-tolerance　meters　is　verified　by　the　meter　data　of　two　platform　areas　with　different　scales.　Compared　with　the　algorithm　of　limited　memory　least　square　method　and　dynamic　line　loss　combined　with　the　FMRLS,　the　proposed　method　has　a　higher　error　estimation　accuracy　with　only　10.6%　false　detection　rate.　Good　error　estimation　results　can　be　obtained　by　relying　on　one　daily　electric　quantity　data.　©　2022　Power　System　Technology　Press.　All　rights　reserved.