With　the　extensive　establishment　of　advanced　metering　infrastructure　(AMI)　and　the　development　of　big　data　technology,　large　volume　of　electricity　load　profile　data　collected　from　smart　meter　reveal　information　about　customer＇s　electricity　consumption　behavior.　The　precise　knowledge　of　customer＇s　load　profiles　clustering　technology　is　helpful　for　power　company　to　develop　differentiated　user　services　and　reasonable　power　dispatching.　In　general,　customers＇s　load　profiles　exist　several　typical　load　profiles(TLPs)　and　a　few　abnormal　or　irregular　load　profiles,　but　the　conventional　clustering　method　can　not　distinguish　them　accurately.　Therefore,　this　paper　proposes　an　approach　based　on　piecewise　symbolic　aggregation.　Firstly,　this　method　reduces　the　dimension　of　load　profiles　by　a　time　series　segmentation　method　based　on　Pearson　correlation　coefficient　(PCC),　and　all　load　profiles　are　divided　into　several　subsequences.　Then,　each　sub-sequences　is　replaced　by　a　character　according　to　the　size　of　each　sub-sequence＇s　mean　value.　After　that,　each　load　profile　will　be　replaced　by　a　symbolic　string,　and　the　load　profiles　with　same　symbolic　string　will　be　cluster　into　a　group.　Finally,　this　paper　performs　a　case　analysis　with　a　Irish　dataset,　and　the　results　show　that　the　proposed　approach　can　improve　the　clustering　quality　of　electrical　load　profiles.　©　2021　IEEE.