Open AccessA data mining model for building occupancy estimation based on deep learning methods
Author(s) -
Yaping Zhou,
Yu Zhao,
Jun Li,
Yuanjian Huang,
Guoqiang Zhang
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/609/7/072029
Subject(s) - occupancy , computer science , estimation , complementarity (molecular biology) , artificial neural network , artificial intelligence , deep learning , machine learning , data mining , engineering , systems engineering , architectural engineering , genetics , biology
Real-time occupancy estimation is of great importance to improve systems control and energy efficiency of buildings. This study proposed a novel real time occupancy estimation model based on CO 2 concentration data. A non-neural-network deep learning method (i.e. gcForest) was used to estimate the number of occupants. The gcForest incorporates three classifiers in each level, enabling the estimation performance to be enhanced by exploiting the complementarity among different learning algorithms. To evaluate the effectiveness of the proposed model, this study conducted an experiment in an office room and compared its results with the IHMM model that was widely used in previous studies. The experimental results indicate that the proposed model could achieve higher estimation accuracy and higher detection accuracy of occupant presence or absence.