基于LSTM预测与K-means聚类的智能家居能效优化

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中图分类号：TP273 文献标识码：A

文章编号：2096-4706（2025）17-0045-07

Abstract： This paper studies the design and implementation of a smart home remote electrical energy efficiency optimizationsystem basedonartificialinteligenceandanembeddedsystem.Thesystemtakes STM32F103C8T6as the main controler，uses ESP32as thecommunicationmodule，and isequipped withmultiplesensordevices forreal-timeacquisition ofenvironmentalinformationandelectricaldata.BydesigninganimprovedLSTMtimeseriespredictionmodel，theaccurate prediction ofelectricalenergyconsumptionisralized.Ituses K-means clusteringanalysis to generateuserclustering strategy， andcombines Machine Learning algorithmto processand analyze thecolecteddata.The experimentalresultsshow that the improved model effectively reduces the prediction eror，successfully identifies several typical power consumptionmodes， significantlyreduces thesystemenergyconsumption，andcanefectivelypredict thetrendofequipmentenergyconsumption.In addition，thesystemrealizesdatavisualizationandremotecontrolfunctionsthroughaWeChatminiprogram，whichprovidesan intelligent solution for household energy efficiency management.

Keywords： smart home; energy efficiency optimization; sensor; time series prediction; clustering

0 引言

随着智能家居技术的迅猛发展，家庭能源管理向着更智能化、自动化的方向发展。（剩余7973字）