Monitoring　electricity　consumption　(EC)　under　high　spatiotemporal　resolution　is　important　for　optimizing　energy　utilization.　Although　current　studies　have　established　various　regression　models　to　spatialize　EC　using　Nighttime　light　(NTL)　imagery　under　the　accepted　assumption　that　EC　is　directly　related　with　NTL,　the　impact　of　other　factors　on　the　EC-NTL　relationship　is　not　fully　considered.　In　this　study,　we　introduced　land　use　types　and　landscape　patterns　to　optimize　EC-NTL　model.　Specifically,　we　constructed　12　indicators　using　NTL　and　Landsat　TM/ETM　+　imageries　to　model　EC,　which　considers　both　human　activity　and　landscape　patterns　on　different　land　use　types.　A　multi-scale　geographically　weighted　regression　(MGWR)　model　was　applied　to　build　the　heterogeneous　relationship.　Experiments　were　conducted　on　292　prefecture-level　cities　in　China.　Results　showed　that　the　MGWR　model　gets　an　adjusted　R2　as　0.92,　which　is　13.6%　and　8.2%　higher　than　the　simple　and　multiple　linear　regression.　Five　indicators　obviously　correlated　with　EC　were　pinpointed,　including　the　total　area　of　urban　and　industrial-mining　land,　NTL　mean,　NTL　standard　deviation　and　patch　density　of　rural　settlement.　It　is　argued　that　scale　is　an　effective　proxy　at　urban　and　industrial-mining　land,　while　NTL　distribution　is　representative　at　rural　settlement　for　modelling　EC.　©　2021　Elsevier　Ltd