With　the　development　of　advanced　metering　infrastructure　(AMI),　electrical　data　are　collected　frequently　by　smart　meters.　Consequently,　the　load　data　volume　and　length　increase　dramatically,　which　aggravates　the　data　storage　and　transmission　burdens　in　smart　grids.　On　the　other　hand,　for　event　detection　or　market-based　demand　response　applications,　load　service　entities　(LSEs)　want　smart　meter　readings　to　be　classified　in　specific　and　meaningful　types.　Considering　these　challenges,　a　stacked　auto-encoder　(SAE)-based　load　data　mining　approach　is　proposed.　First,　an　innovative　framework　for　smart　meter　data　flow　is　established.　On　the　user　side,　the　SAEs　are　utilized　to　compress　load　data　in　a　distributed　way.　Then,　centralized　classification　is　adopted　at　remote　data　center　by　softmax　classifier.　Through　the　layer-wise　feature　extracting　of　SAE,　the　sparse　and　lengthy　raw　data　are　expressed　in　compact　forms　and　then　classified　based　on　features.　A　global　fine-tuning　strategy　based　on　a　well-defined　labeled　subset　is　embedded　to　improve　the　extracted　features　and　the　classification　accuracy.　Case　studies　in　China　and　Ireland　demonstrate　that　the　proposed　method　is　more　capable　to　achieve　the　minimum　of　error　and　satisfactory　compression　ratios　(CR)　than　benchmark　compressors.　It　also　significantly　improves　the　classification　accuracy　on　both　appliance　and　house　level　datasets.