Load　prediction　is　an　essential　technique　to　improve　edge　system　performance　by　proactively　configuring　and　allocating　system　resources.　Traditional　load　prediction　methods　obtain　high　prediction　when　handling　loads　exhibiting　cyclical　trend　behavior,　but　they　are　unable　to　capturing　highly-variable　loads　in　edge　computing　environments.　Existing　studies　fit　prediction　models　via　independent　time　series　and　output　single-point　real-value　predictions.　However,　in　practical　edge　scenarios,　it　is　more　valuable　to　obtain　application　value　by　utilizing　the　probability　distribution　of　future　loads　rather　than　directly　predicting　specific　values.　To　solve　these　problems,　we　propose　an　Edge　Load　Prediction　method　empowered　by　Deep　Auto-regressive　Recurrent　networks　(ELP-DAR).　The　ELP-DAR　uses　the　time-series　data　of　edge　loads　to　train　deep　auto-regressive　recurrent　networks,　which　integrate　Long　Short-Term　Memory　(LSTM)　into　the　S2S　framework　to　calculate　the　parameters　of　the　probability　distribution　at　the　next　time-point.　Therefore,　the　ELP-DAR　can　efficiently　extract　the　essential　representations　of　edge　loads　and　learn　their　complex　patterns,　and　the　probability　distribution　for　highly-variable　edge　loads　can　be　accurately　predicted.　Extensive　simulation　experiments　are　conducted　to　validate　the　effectiveness　of　the　proposed　ELP-DAR　method　based　on　real-world　edge　load　datasets.　The　results　show　that　the　ELP-DAR　achieves　higher　prediction　accuracy　than　other　benchmark　methods　with　different　prediction　lengths.