Research on Intelligent Control Method for Air Conditioning Based on LBD-WA Feedback Enhancement
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.
Multiple feedback correctionMultiple Linear RegressionBayesian networkDecision TreeAbnormal adjustment feedbackTime zone and seasonal feedbackPhysiological change feedback
万方数据
维普
