Paper Conference

Proceedings of Building Simulation 2021: 17th Conference of IBPSA

     

Improving indoor environmental quality through smart operable windows; a machine learning approach

Farimah Moezzi 1, Amirhossein Fathi 2
1 Shahid beheshti university, Iran, Islamic Republic of Iran
2 Shiraz University, Iran, Islamic Republic of Iran


DOI: https://doi.org/10.26868/25222708.2021.31010
Abstract: People spend 90% of their time indoors, and this fact highlights the importance of Indoor Air Quality (IAQ) in living, working, and educational environments. In today's smart buildings, thermal comfort is directly related to indoor air quality and indoor environmental quality (IEQ), which in turn depends on weather conditions such as air velocity, solar heat gain, and relative humidity. IAQ improvement is obtained by different methods of controlling the parameters of the internal conditions in buildings. The effect of IAQ on thermal comfort and occupant's function is undeniably entwined with one another. This research aims to find the relations between IAQ parameters and occupant's behaviour and preferences using a smart controller for windows. The study attempts to realize the proposed window controller's accuracy and affordability to assess IAQ parameters impacted by occupant's autonomy via availing of one of the most effective deep learning techniques, Artificial Neural Network (ANN). This study is conducted in an educational building.
Keywords: IEQ, machine learning, operable window, occupants behavior
Pages: 2860 - 2867
Paper:
bs2021_31010