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首页> 外文期刊>ACM transactions on sensor networks >A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO_2 Sensor Data
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A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO_2 Sensor Data

机译：具有可转移时间序列分解的CO_2传感器数据的可扩展房间占用率预测

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摘要

Human occupancy counting is crucial for both space utilisation and building energy optimisation. In the current article, we present a semi-supervised domain adaptation method for carbon dioxide - Human Occupancy Counter Plus Plus (DA-HOC++), a robust way to estimate the number of people within one room by using data from a carbon dioxide sensor. In our previous work, the proposed Seasonal Decomposition for Human Occupancy Counting (SD-HOC) model can accurately predict the number of individuals when the training and labelled data are adequately available. DA-HOC++ is able to predict the number of occupants with minimal training data: as little as 1 day's data. DA-HOC++ accurately predicts indoor human occupancy for five different rooms across different countries using a model trained from a small room and adapted to other rooms. We evaluate DA-HOC++ with two baseline methods: a support vector regression technique and an SD-HOC model. The results demonstrate that DA-HOC++'s performance on average is better by 10.87% in comparison to SVR and 8.65% in comparison to SD-HOC.
机译：人员占用计数对于空间利用和建筑能源优化都至关重要。在当前文章中，我们提出了一种针对二氧化碳的半监督域自适应方法-人类占有率增强（DA-HOC ++），这是一种通过使用二氧化碳传感器中的数据来估算一个房间中人数的可靠方法。在我们之前的工作中，当有足够的训练数据和标签数据时，拟议的人类居住计数季节性分解（SD-HOC）模型可以准确预测个体数量。 DA-HOC ++能够以最少的培训数据来预测乘员人数：只需1天的数据。 DA-HOC ++使用从一个小房间训练并适应其他房间的模型，准确地预测了不同国家/地区的五个不同房间的室内人类居住情况。我们用两种基线方法评估DA-HOC ++：支持向量回归技术和SD-HOC模型。结果表明，DA-HOC ++的平均性能比SVR高10.87％，比SD-HOC高8.65％。

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