Accurate　time　series　forecasting　of　NOx　concentration　in　flue　gas　is　an　important　part　of　MSWI　(municipal　solid　waste　incineration).　Time　series　prediction　methods　based　on　sliding　window　prediction,　such　as　CNN　and　LSTM,　have　the　problem　of　error　accumulation,　that　is,　the　longer　the　predicted　time　series,　the　greater　the　error.　Aiming　at　the　above　problems,　we　innovatively　introduce　Informer　for　time　series　prediction　of　NOx　concentration.　In　this　paper,　we　propose　a　method　in　which　Informer　fused　with　learnable　positional　encoding　is　trained　with　the　dataset　generated　with　variables　selected　by　TLCC　(time-lagged　cross-correlation)　between　NOx　concentration　time　series　and　other　parameter　time　series　to　achieve　time　series　forecasting　of　NOx　concentration.　Firstly,　the　TLCC　was　used　to　select　the　appropriate　variables　to　generate　the　data　set.　Secondly,　the　global　timestamp　encoding　of　Informer　is　discarded,　and　the　experiment　proves　that　the　global　timestamp　does　not　help　the　time　series　forecasting　of　NOx　concentration.　Thirdly,　the　learnable　positional　encoding　is　integrated　into　Informer　to　enhance　the　model＇s　flexibility　to　obtain　the　location　information　of　data　at　different　time　steps　to　compensate　for　the　lack　of　the　global　timestamp.　Finally,　the　prediction　results　of　the　proposed　method　are　compared　with　those　of　other　models　and　methods　using　several　evaluation　indicators.　The　experimental　results　show　that　this　method　has　better　prediction　ability.　©　2023　IEEE.