Indoor CO2 Level-Based Occupancy Estimation at Low-Scale Occupant using Statistical Learning Method

被引：1

作者：

Rahman, Haolia ^{[1
]}

Abdillah, Abdul Azis ^{[1
]}

Apriana, Asep ^{[1
]}

Handaya, Devi ^{[1
]}

Assagaf, Idrus ^{[1
]}

机构：

[1] Politekn Negeri Jakarta, Dept Mech Engn, Depok, Indonesia

来源：

2021 4TH INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATICS ENGINEERING (IC2IE 2021) | 2021年

关键词：

occupancy estimation; carbon dioxide; statistical learning; low-scale occupants; ROOM;

D O I：

10.1109/IC2IE53219.2021.9649072

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Most of the occupancy estimations based on indoor CO2 levels are tested on a large-scale number of occupants such the order of tens or hundreds. Logically because the pattern of the occupancy and CO2 level is about similar at a broad range of occupants. In the present study, a small office room with an occupancy scale of 0-6 people was tested. The Statistical Learning method is used to estimate the number of occupants, including Decision Tree, Random Forest classifier, SVM, Logistic regression, K-Nearest Neighbor, and Neural Network. A combination of training and testing data set is applied to the methods and a comparison has been made in order to distinguish their accuracy. The result shows that the accuracy of self-estimation and cross-estimation is ranged from 86-100% and 86-94% respectively. It also found that the estimation accuracy of self-and-cross validation does not significantly increase with the increase of data set combination.

引用

页码：496 / 499

页数：4

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