基于iTransformer与LSTM模型融合的农场气温多步预测

打开文本图片集

关键词：iTransformer;LSTM；模型融合；多特征；农场气温；多步预测

中图分类号：TP183 文献标识码：A

文章编号：0439-8114（2025）05-0134-07

DOI：10.14088/j.cnki.issn0439-8114.2025.05.021 开放科学（资源服务）标识码（OSID）：

Multi-step temperature prediction for farms based on iTransformer and LSTM model fusion

XIE Qi， ZHANG Tai-hong，LIU Hai-peng

（Colegefftteala of Intelligent Agriculture，MinistryofEducation，Xinjiang Agricultural University，Urumqi830o52，China）

Abstract：Toadressthenonlinearandomplexcharacteristicsoffarmtemperaturedata，basedonmeteorologicalstationdatafrom HuaxingFarminChangji City，XinjiangUygurAutonomous Region，sevenfeaturesincludingtemperature，groundinfraredtemperature，dewpointtemperaturerelativehumidityaporpressure，stationpressure，andsea-levelpressurewereselectedasmodelinput features throughSpearancorelationanalysis，andomparativeanalsisascoductedamongtheiransformer-LSTodelras former model，LSTMmodel，iTransformermodel，andTransformer-LSTMmodel.TheresultsshowedthattheiTransformer-TM modelachievedthebestperformance.Comparedwiththeoptimal baselinemodeliTransformer，thismodelreducedtherootmean square error（RMSE）by 13.72% ，mean absolute error （ MAE ）by 14.12% ，and mean absolute percentage error （ MAPE ）by 13.61% TheiTransformer-LSTMmodelcouldefectivelyextracttime-series featurerepresentations，capturelong-termdependencies，and characterize globalaturesandcontextualinformation，makingitsuitableforulti-featureulti-steptimeseries temperatureprediction tasks.

Key Words：iTransformer;LSTM;model fusion；multi-feature; farm temperature；multi-step prediction

气候变化可能引发极端天气事件，如干旱、洪涝、低温和霜冻，这些现象对农业生产造成严重风险。（剩余9038字）