An action-based Markov chain modeling approach for predicting the window operating behavior in office spaces

• 作者: Xin Zhou Tiance Liu Da Yan Xing Shi Xing Jin
• 来源期刊: 建筑模拟（英文版）
• 年，卷（期）: 2021,（2）
• 所属期刊栏目: Research Articles
• 页码范围: 301-315
• 页数: 15页
• 语种: 英文

摘要

Reliable energy and performance prediction for building design and planning is important for newly-designed or retrofitted buildings. Window operating behavior has an important influence on the ventilation and energy consumption of these buildings under different realistic scenarios. Therefore, quantitatively describing this behavior and constructing a prediction model are important. In this work, an action-based Markov chain modeling approach for predicting window operating behavior in office spaces was proposed. Two summer measurement data (2016 and 2018) were used to verify the accuracy and validity of the modeling approach. The opening rate, outdoor temperature, time distribution, and on-off curve were proposed as four inspection standards. This study also compared the prediction performance between the action-based Markov chain modeling approach with the state-based Markov chain modeling approach, which is the most popular modeling approach to model occupant window operating behavior. This study proved that the yearly variation of occupants' behavior performed a form of action that remained unchanged during a certain period. Meanwhile, the results also proved that the action- based Markov chain modeling approach can reflect the actual window operating behavior accurately within an open-plan office, which is a beneficial supplement for energy- consumption simulation software in a window-state prediction module. The state-based Markov chain modeling approach showed better stability and accuracy in terms of the opening rate, whereas the action-based Markov chain modeling approach showed good consistency with the measurement data in the on-off curves and in situations with little data. For the on-off curves, the accuracy of action-based modeling approach in the prediction of window open-state is 20％ higher.