A methodology based on Hidden Markov Models for occupancy detection and a case study in a low energy residential building
A methodology based on Hidden Markov Models for occupancy detection and a case study in a low energy residential building
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DOI：
10.1016/j.enbuild.2017.05.031
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发表时间：
2017-08-01
影响因子：
6.7
通讯作者：
Deramaix, Dominique
中科院分区：
文献类型：
--
作者：
Candanedo, Luis M.;Feldheim, Veronique;Deramaix, Dominique
本文提出并评估了一个简单的方法，基于隐马尔可夫模型的问题，无监督占用检测使用开源程序R。这些模型是使用不同的环境参数创建的，如温度、湿度、湿度比、二氧化碳和光照时间序列数据，并根据公共数据集的地面实况占有率进行评估。模型的准确性报告。此外，作为一个案例研究，所开发的方法适用于湿度比数据计算的温度和湿度测量在不同的房间（厨房，客厅，办公室，父母的房间，青少年的房间，洗衣房，熨衣室和浴室）在低能耗的住宅楼，以推断每日和每小时的平均占用时间表，没有地面实况数据。估计的入住时间表是由一个房子的住户评论和讨论。用这种方法发现的推断时间表可能有助于理解平均占用时间表，用于检测定期活动或行动，并作为住宅建筑能源模拟的输入。(C)2017爱思唯尔B.V.保留所有权利。
This paper presents and evaluates a simple methodology based on Hidden Markov models for the problem of unsupervised occupancy detection using the open source program R. The models were created using different environmental parameters such as temperature, humidity, humidity ratio, CO2 and light time series data and were evaluated against ground truth occupancy from a public data set. The accuracies of the models are reported. Also, as a case study, the developed methodology is applied for humidity ratio data calculated from temperature and humidity measured in different rooms (kitchen, living room, office, parents' room, teenager's room, laundry room, ironing room and bathroom) in a low energy residential building to infer daily and hourly average occupancy schedules for which there is no ground truth data. The estimated occupancy schedules are commented on by one of the house occupants and discussed. Inferred schedules found with this method could be useful for understanding average occupancy schedules, for detecting regular activities or actions and as an input for residential building energy simulations. (C) 2017 Elsevier B.V. All rights reserved.