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.

对于建筑物的设计和规划，可靠的能源和性能预测对于新建或翻新的建筑物至关重要。在不同的实际情况下，窗户的运行行为对这些建筑物的通风和能耗有重要影响。因此，定量描述这种行为并构建预测模型非常重要。在这项工作中，提出了一种基于动作的马尔可夫链建模方法来预测办公室空间中的窗户操作行为。使用两个夏季测量数据（2016年和2018年）来验证建模方法的准确性和有效性。提出了四种检验标准：开门率，室外温度，时间分布和开关曲线。这项研究还比较了基于动作的马尔可夫链建模方法和基于状态的马尔可夫链建模方法之间的预测性能，后者是用于对乘员窗操作行为进行建模的最流行的建模方法。该研究证明，乘员行为的年度变化执行了某种形式的动作，该动作在一定时期内保持不变。同时，结果还证明，基于动作的马尔可夫链建模方法可以在开放式办公室内准确反映实际的窗口操作行为，这是对窗口状态预测模块中的能耗模拟软件的有益补充。基于状态的马尔可夫链建模方法显示出更好的稳定性和准确性（开孔率），而基于动作的马尔可夫链建模方法在开关曲线和数据很少的情况下与测量数据显示出良好的一致性。对于开关曲线，基于动作的建模方法在预测窗口打开状态方面的准确性要高20％。