基于递归神经网络的智能建筑精确轨迹预测

Anooshmita Das, Emil Stubbe Kolvig Raun, M. Kjærgaard
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引用次数: 3

摘要

居住者的行为模式一旦被提取出来,就可以揭示有关活动和空间使用的线索,这些线索可以有效地用于建筑系统,以实现节能。准确预测房间内不同区域的居住者轨迹的能力有许多值得注意和引人注目的应用。例如,高效的空间利用和楼层规划、智能建筑操作、人群管理、舒适的室内环境、安全、疏散或人员管理。本文提出了未来乘员轨迹预测使用最先进的时间序列预测方法,即长短期记忆(LSTM)和门控循环单元(GRU)模型。这些模型正在实施,并以非侵入性和可靠的方式与给定时间和地点的预测乘员轨迹进行比较。考虑的数据集收集的测试空间是一个仪器公共建筑中的多功能区域。部署的3D立体视觉摄像头从鸟瞰角度捕捉空间位置坐标(x坐标和y坐标),而不会引发任何其他可能泄露机密数据或唯一识别一个人的信息。结果表明,GRU模型的轨迹预测精度明显高于LSTM模型。对于监测区域内的多个乘员轨迹,GRU预测模型与预测位置坐标的均方误差(Mean Squared Error, MSE)分别为30.72 cm和47.13 cm。采用另一个评价指标平均绝对误差(MAE), GRU预测模型的MAE为3.14 cm, LSTM模型的MAE为4.07 cm。与基线LSTM模型相比,GRU模型保证了任何给定情况下的高保真乘员轨迹预测,精度更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accurate trajectory prediction in a smart building using recurrent neural networks
Occupant behavioral patterns, once extracted, could reveal cues about activities and space usage that could effectively get used for building systems to achieve energy savings. The ability to accurately predict the trajectories of occupants inside a room branched into different zones has many notable and compelling applications. For example - efficient space utilization and floor plans, intelligent building operations, crowd management, comfortable indoor environment, security, and evacuation or managing personnel. This paper proposes future occupant trajectory prediction using state-of-the-art time series prediction methods, i.e., Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models. These models are being implemented and compared to forecast occupant trajectories at a given time and location in a non-intrusive and reliable manner. The considered test-space for the collection of the dataset is a multi-utility area in an instrumented public building. The deployed 3D Stereo Vision Cameras capture the spatial location coordinates (x- and y- coordinates) from a bird's view angle without eliciting any other information that could reveal confidential data or uniquely identify a person. Our results showed that the GRU model forecasts were considerably more accurate than the LSTM model for the trajectory prediction. GRU prediction model achieved a Mean Squared Error (MSE) of 30.72 cm between actual and predicted location coordinates, and LSTM achieved an MSE of 47.13 cm, respectively, for multiple occupant trajectories within the monitored area. Another evaluation metric Mean Absolute Error (MAE) is used, and the GRU prediction model achieved an MAE of 3.14 cm, and the LSTM model achieved an MAE of 4.07 cm. The GRU model guarantees a high-fidelity occupant trajectory prediction for any given case with higher accuracy when compared to the baseline LSTM model.
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