现代信息科技2025，Vol.9Issue(17)：45-51,7.DOI:10.19850/j.cnki.2096-4706.2025.17.009
基于LSTM预测与K-means聚类的智能家居能效优化
Smart Home Energy Efficiency Optimization Based on LSTM Prediction and K-means Clustering
摘要
Abstract
This paper studies the design and implementation of a smart home remote electrical energy efficiency optimization system based on artificial intelligence and an embedded system.The system takes STM32F103C8T6 as the main controller,uses ESP32 as the communication module,and is equipped with multiple sensor devices for real-time acquisition of environmental information and electrical data.By designing an improved LSTM time series prediction model,the accurate prediction of electrical energy consumption is realized.It uses K-means clustering analysis to generate user clustering strategy,and combines Machine Learning algorithm to process and analyze the collected data.The experimental results show that the improved model effectively reduces the prediction error,successfully identifies several typical power consumption modes,significantly reduces the system energy consumption,and can effectively predict the trend of equipment energy consumption.In addition,the system realizes data visualization and remote control functions through a WeChat mini program,which provides an intelligent solution for household energy efficiency management.关键词
智能家居/能效优化/传感器/时序预测/聚类Key words
smart home/energy efficiency optimization/sensor/time series prediction/clustering分类
信息技术与安全科学引用本文复制引用
高云端,黄庭炘,田俊慧,郑凯泽..基于LSTM预测与K-means聚类的智能家居能效优化[J].现代信息科技,2025,9(17):45-51,7.基金项目
大学生创新创业训练计划项目(X202410251195) （X202410251195）
