The　latest　development　of　smart　grid　technologies　gives　rise　to　big　load　data　and　requires　load　pattern　categorization　(LPC).　How　to　determine　a　precise　cluster　number　and　choose　an　appropriate　clustering　algorithm　are　critical　and　still　remain　challenging　in　LPC.　In　this　work,　we　propose　a　novel　parametric　bootstrap　(PB)　algorithm　to　address　the　cluster　number　determination　problem　in　load　pattern　analysis.　The　proposed　PB　algorithm　is　more　robust　against　dimensionality　of　data　and　more　applicable　for　the　load　demand　data　which　is　usually　of　high　dimensionality.　The　PB　algorithm　is　also　general　and　independent　of　data　type,　resulting　in　a　more　precise　cluster　number　determined　than　existing　methods　with　little　fluctuation.　Moreover,　an　effective　cascade　clustering　scheme　is　proposed　to　categorize　load　demand　data　and　analyze　load　patterns,　based　on　the　PB　algorithm　and　the　K-means++　clustering　algorithm.　The　results　indicate　the　feasibility　and　the　superiority　of　the　proposed　approach.　©　2019