To　alleviate　the　concept　drift　of　an　offline　prediction　model　for　the　furnace　temperature　in　a　municipal　solid　waste　incineration　(MSWI)　process　caused　by　changes　in　working　conditions,　a　prediction　method　for　the　furnace　temperature　based　on　a　knowledge　transfer　online　stochastic　configuration　network　is　proposed　in　this　work.　The　proposed　method　includes　offline　learning　and　online　learning.　First,　a　stochastic　configuration　network　is　utilized　to　construct　the　offline　furnace　temperature　prediction　model,　and　a　knowledge　transfer　method　is　employed　to　update　the　model　under　new　operating　conditions　in　the　offline　learning　stage.　Then,　the　updated　model　is　used　as　the　initial　state　of　online　modeling.　Second,　the　recursive　solution　of　the　model　output　weights　is　presented　to　adapt　to　the　dynamic　change　in　incineration　conditions,　and　a　direction　forgetting　mechanism　is　utilized　to　enhance　the　result　of　the　prediction　model　for　the　furnace　temperature　under　nonpersistence　of　excitation　in　the　online　stage.　Finally,　to　further　verify　the　proposed　online　modeling　method,　the　real　historical　data　of　an　MSWI　plant　are　utilized　to　finish　the　comparative　experiments.　The　experimental　results　with　the　other　methods　show　that　the　proposed　prediction　method　for　the　furnace　temperature　presents　a　smaller　error.　Hence,　the　proposed　method　can　reduce　the　influence　of　working　conditions　on　the　accuracy　of　furnace　temperature　prediction　models.