This article proposes an intelligent temperature and air conditioning control algorithm to address the limitations of traditional air conditioning temperature control methods in meeting the diverse needs of different us-ers and scenarios.The algorithm combines trend prediction using Multiple Linear Regression,environmental change correction using Bayesian network,and user characteristic correction using Decision Tree to form a tem-perature adjustment model that comprehensively considers multiple factors.By using the Weighted Average method with three in one correction,and becomes the LBD-WA feedback enhancement model.It not only considers real-time environmental changes but also adjusts according to the user's temperature preferences and seasonal changes in their time zone to provide a more accurate and comfortable temperature environment.To validate the effectiveness of this algorithm,comparative experiments were conducted between this model and other machine learning meth-ods.The results show that the LBD-WA model performs well in terms of prediction accuracy and adaptability to multiple scenarios,with a 6%improvement in accuracy compared to traditional air conditioning methods.This pro-vides a new direction for temperature control technology in future smart homes.
关键词
多重反馈修正/多元线性回归/贝叶斯网络/决策树/异常调节反馈/时区季节反馈/体征变化反馈
Key words
Multiple feedback correction/Multiple Linear Regression/Bayesian network/Decision Tree/Abnormal adjustment feedback/Time zone and seasonal feedback/Physiological change feedback
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