基于住户差异性的住宅建筑在室行为预测模型

A Resident-differentiated Model for Prediction of Occupancy in Residential Buildings

现有住宅建筑在室行为预测模型缺乏对住户差异性的合理考虑,导致模型往往存在整体预测精度不高和适用性受限等问题.针对这一问题,提出一种考虑住户差异性的马尔可夫链在室状态预测模型.该模型首先通过Spearman相关性分析确定了不同影响因素(即特征参数)与住户总在室时长的相关性,将相关系数作为特征参数权值并结合聚类分析对住户群体进行分类.在此基础上采用马尔可夫链模型对住户在室状态进行预测.为评估所建立预测模型的性能,以英国TUS(Time Use Survey)数据库为例,将改进模型与传统马尔可夫链模型进行对比分析.结果表明,该方法能够综合考虑不同住户特征参数及其对在室行为的影响,对住户进行合理的分类,与传统马尔可夫模型相比,所建预测模型显著提升了整体性能,平均绝对误差和均方根误差分别减小了20.57%和15.35%.

Existing occupancy prediction models for residential buildings often lack the reasonable consideration of resident diversity,which generally results in poor prediction accuracy and limited applicability.To address this issue,this study proposes a Resident-differentiated,Markov Chain Occupancy Prediction Model with Cluster(RMCPMC)analysis to fully consider the resident diversity so as to improve the model predictive performance.First,Spearman correlation analysis is employed to identify the correlation between different influencing factors(i.e.resident characteristics)and total occupancy duration.The identified correlation coefficients are used as the weights for corresponding factors,and cluster analysis is subsequently performed to classify residents into different groups.Finally,RMCPMC models are established for obtained clusters to predict the occupancy pattern.To validate the performance of the proposed model,it is applied to the UK Time Use Survey(TUS)dataset and its performance is compared with the conventional Markov Chain(MC)model.Compared with the conventional MC model,the Mean Absolute Error and the Root Mean Square Error of the prediction accuracy decrease by 20.57%and 15.35%,respectively.The results indicate a significant improvement in model prediction accuracy through reasonably considering resident diversity and their impacts on occupancy patterns.