A new modeling approach for short-term prediction of occupancy in residential buildings
A new modeling approach for short-term prediction of occupancy in residential buildings
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DOI：
10.1016/j.buildenv.2017.05.005
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发表时间：
2017-08
影响因子：
7.4
通讯作者：
Zhaoxuan Li;B. Dong
中科院分区：
文献类型：
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作者：
Zhaoxuan Li;B. Dong
占用模型是智能建筑设计和运行的必要条件。开发一种适当的算法来预测占用率，可以更好地控制和优化整个建筑的能耗。然而，以往对该模式开发的研究大多集中在商业建筑上。住宅的占用模型通常基于时间用户调查数据。这项研究的重点是提供一个独特的数据集的四个住宅收集从占用传感器。提出了一种新的非齐次马尔可夫入住率预测模型，并与概率抽样、人工神经网络和支持向量回归等常用模型进行了比较。基于历史数据的变化点分析，优化了存在预测的训练周期。在不同的时间情景下，包括提前15分钟、提前30分钟、提前1小时和提前24小时，进一步探讨和评估了模型的预测能力。并在房间和房屋两个层面对预测精度进行了空间比较。最终结果表明，所提出的马尔可夫模型在15分钟前预测入住率的平均正确率为5%，最大误差为11%，优于其他方法。然而，在24小时前的预报中没有观察到太大的差异。
Occupancy models are necessary towards design and operation of smart buildings. Developing an appropriate algorithm to predict occupancy presence will allow a better control and optimization of the whole building energy consumption. However, most previous studies of development of such model only focus on commercial buildings. The occupancy model of residential houses are usually based on Time User Survey data. This study focuses on providing a unique data set of four residential houses collected from occupancy sensors. A new inhomogeneous Markov model for occupancy presence prediction is proposed and compared to commonly used models such as Probability Sampling, Artificial Neural Network, and Support Vector Regression. Training periods for the presence prediction are optimized based on change-point analysis of historical data. The study further explores and evaluates the predictive capability of the models by various temporal scenarios, including 15-min ahead, 30-min ahead, 1-hour ahead, and 24-hour ahead forecasts. The spatial-level comparison is additionally conducted by evaluating the prediction accuracy at both room-level and house-level. The final results show that the proposed Markov model outperforms the other methods in terms of an average 5% correctness with 11% maximum difference in 15-min ahead forecast of the occupancy presence. However, there is not much differences observed for 24-hour ahead forecasts.