建筑用户在室行为预测新方法

准确预测建筑用户在室行为可显著提高建筑能耗模拟精度,并进一步帮助建筑设计及运行控制优化.当前进行在室行为预测时所采用的主要是基于隐马尔可夫链方法的数学模型,该模型考虑了在室行为的时间关联性,可平稳有效地预测在室行为.然而现有隐马尔科夫模型难以准确描述在室行为动态变化规律以及在室行为与可观测参数之间的关联,降低了模型预测精度.针对该问题,本文提出一种基于状态转移的时变隐马尔科夫模型.该模型采用时变状态转移概率矩阵量化不同时刻在室行为的动态变化特征及关联,同时该模型基于状态转移计算可观测参数的概率分布以定量描述在室行为对可观测参数的影响.本文采用比利时某办公室在室行为数据库进行了相关建模和验证,结果表明该模型可更有效地捕捉在室状态变化,从而提高了在室行为预测精度.

A New Approach for Building Occupancy Prediction

Accurate prediction of occupancy in buildings can significantly improve the performance of building energy simulation and further facilitate building design and system operation. Considering the temporal dependency of occupancy, Hidden Markov Model has been widely used to effectively predict occupancy behavior. However, the traditional Hidden Markov model that uses time-independent transition probability matrix is difficult to accurately describe the dynamic variation of occupancy as well as its correlation with environmental parameters. Such a model would greatly reduce occupancy prediction accuracy. To address this issue, an inhomogeneous Hidden Markov Model based on state transition was proposed. In this model, time-dependent transition probability matrices were calculated to capture the temporal dependency of occupancy at different time periods. Meanwhile, probability distribution of environmental parameters was calculated based on state transition instead of state only, aiming at rationally describing the correlation between occupancy and environmental parameters. The method was applied to predict the occupancy of a Belgian office. The results demonstrated that the effectiveness of the proposed approach and the prediction accuracy were improved significantly.