In　order　to　provide　more　efficient　and　reliable　power　services　than　the　traditional　grid,　it　is　necessary　for　the　smart　grid　to　accurately　predict　the　electric　load.　Recently,　recurrent　neural　networks　(RNNs)　have　attracted　increasing　attention　in　this　task　because　it　can　discover　the　temporal　correlation　between　current　load　data　and　those　long-ago　through　the　self-connection　of　the　hidden　layer.　Unfortunately,　the　traditional　RNN　is　prone　to　the　vanishing　or　exploding　gradient　problem　with　the　increase　of　memory　depth,　which　leads　to　the　degradation　of　predictive　accuracy.　Many　RNN　architectures　address　this　problem　at　the　expense　of　complex　internal　structures　and　increased　network　parameters.　Motivated　by　this,　this　article　proposes　two　adaptive　modularized　RNNs　to　tackle　the　challenge,　which　can　not　only　solve　the　gradient　problem　effectively　with　a　simple　architecture,　but　also　achieve　better　performance　with　fewer　parameters　than　other　popular　RNNs.　©　2023　IGI　Global.　All　rights　reserved.