Building occupancy estimation holds great promise for building control systems to save energy and provide a comfortable indoor environment. Existing solutions turn out to be lacking in practice due to their specific hardware requirements and/or poor performances. Recently, an light-emitting diode (LED) light sensor based occupancy estimation system, which is nonintrusive and does not require any additional hardware, has been proposed. However, the performance of the system is limited, especially in a complicated dynamic scenario. In this article, a Bayes filter with neural networks is proposed for the optimal estimation of occupancy based on light sensor data. Specifically, based on the formulation of Bayes filter, the posterior probability of the building occupancy can be decoupled into three components: The prior, likelihood, and evidence. The prior and likelihood are, respectively, estimated from a Markov model and an efficient single-hidden layer feedforward neural network (SLFN). Finally, the evidence can be obtained by the results of prior and likelihood. Real experiments have been conducted to verify the effectiveness of the proposed approach in two complicated scenarios, i.e., dynamic and regular. Results indicate that the proposed Bayes filter outperforms all the benchmark approaches. The impacts of the number of LED sensing units and the number of hidden layers for neural networks are also evaluated. The results manifest that the number of sensing units should be chosen based on the required performance and the SLFN is sufficient for this application.

中文翻译：

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通过带神经网络的贝叶斯滤波器基于光传感器的占用估计

楼宇占用率估算对于楼宇控制系统在节能和提供舒适的室内环境方面具有巨大的前景。现有的解决方案由于其特定的硬件要求和/或性能不佳而缺乏实践。最近，已经提出了一种基于发光二极管 (LED) 光传感器的占用估计系统，该系统是非侵入式的并且不需要任何额外的硬件。然而，系统的性能是有限的，尤其是在复杂的动态场景中。在本文中，提出了一种带有神经网络的贝叶斯滤波器，用于基于光传感器数据的最佳占用估计。具体来说，基于贝叶斯滤波器的公式，建筑物占用的后验概率可以分解为三个分量：先验、似然和证据。先验和似然分别由马尔可夫模型和高效的单隐藏层前馈神经网络 (SLFN) 估计。最后，可以通过先验和似然的结果获得证据。已经进行了实际实验以验证所提出的方法在两个复杂场景中的有效性，即动态和规则。结果表明，所提出的贝叶斯过滤器优于所有基准方法。还评估了 LED 传感单元数量和神经网络隐藏层数量的影响。结果表明，应根据所需的性能选择传感单元的数量，并且 SLFN 足以满足此应用的需求。从马尔可夫模型和高效的单隐藏层前馈神经网络 (SLFN) 估计。最后，可以通过先验和似然的结果获得证据。已经进行了实际实验以验证所提出的方法在两个复杂场景中的有效性，即动态和规则。结果表明，所提出的贝叶斯过滤器优于所有基准方法。还评估了 LED 传感单元数量和神经网络隐藏层数量的影响。结果表明，应根据所需的性能选择传感单元的数量，并且 SLFN 足以满足此应用的需求。从马尔可夫模型和高效的单隐藏层前馈神经网络 (SLFN) 估计。最后，可以通过先验和似然的结果获得证据。已经进行了实际实验以验证所提出的方法在两个复杂场景中的有效性，即动态和规则。结果表明，所提出的贝叶斯过滤器优于所有基准方法。还评估了 LED 传感单元数量和神经网络隐藏层数量的影响。结果表明，应根据所需的性能选择传感单元的数量，并且 SLFN 足以满足此应用的需求。已经进行了实际实验以验证所提出的方法在两个复杂场景中的有效性，即动态和规则。结果表明，所提出的贝叶斯过滤器优于所有基准方法。还评估了 LED 传感单元数量和神经网络隐藏层数量的影响。结果表明，应根据所需的性能选择传感单元的数量，并且 SLFN 足以满足此应用的需求。已经进行了实际实验以验证所提出的方法在两个复杂场景中的有效性，即动态和规则。结果表明，所提出的贝叶斯过滤器优于所有基准方法。还评估了 LED 传感单元数量和神经网络隐藏层数量的影响。结果表明，应根据所需的性能选择传感单元的数量，并且 SLFN 足以满足此应用的需求。