In　view　of　the　substantial　amount　of　solid　waste　in　China　and　the　incomplete　combustion　of　solid　waste　in　the　incineration　process　due　to　unstable　combustion　temperatures.　This　leads　to　the　release　of　a　large　amount　of　harmful　gases　such　as　dioxins　and　carbon　monoxide　into　the　atmosphere.　To　ensure　the　complete　combustion　of　solid　waste,　temperature　prediction　control　of　the　incinerator　is　one　of　the　key　measures.　Currently,　temperature　prediction　still　relies　on　obtaining　information　from　individual　time　steps,　which　is　not　ideal　for　long　time　series　temperature　prediction.　To　address　these　issues,　this　paper　proposes　a　temperature　prediction　method　for　Municipal　Solid　Waste　Incinerator　(MSWI)　based　on　the　patch　time　series　transformer.　Firstly,　each　input　univariate　time　series　is　divided　into　patches.　Secondly,　a　standard　Transformer　encoder　is　employed　to　map　the　observed　signals　to　the　latent　representation　space.　The　patches　are　mapped　to　the　Transformer　latent　space　using　a　trainable　linear　projection　matrix,　and　a　learnable　additive　position　encoding　matrix　is　applied　to　monitor　the　temporal　order　of　the　patches.　Additionally,　to　achieve　better　prediction　results,　a　convolutional　layer　is　added　to　the　encoder　for　feature　extraction.　Finally,　a　flatten　layer　with　a　linear　head　is　used　to　obtain　the　prediction　result.　Experimental　results　of　temperature　prediction　based　on　the　Patch　Time　Series　Transformer　model　demonstrate　promising　outcomes　for　long　time　series　with　multiple　temperatures.　©　2024　IEEE.