Research on HVAC Occupancy Detection with ML and DL Methods

DOI

10.2991/978-94-6463-512-6_69How to use a DOI?

Keywords

HVAC; Machine Learning; Deep Learning; Green Building

Abstract

It is necessary to optimize Heating, Ventilating and Air Conditioning (HVAC) system efficiency, significantly reduce energy consumption and costs, and minimize carbon emissions by accurately predicting occupancy patterns using advanced Machine Learning (ML) and Deep Learning (DL) techniques. This research focuses on improving HVAC system efficiency through occupancy detection using ML and DL techniques. The study addresses the critical issue of high energy consumption in buildings, which accounts for about 40% of total energy use, by optimizing HVAC operations to reduce waste and carbon emissions. By predicting occupancy patterns accurately, HVAC systems can be adjusted to provide heating and cooling only when necessary, leading to significant energy savings and cost reductions. The research employs various predictive models, including regression, time series forecasting, and ensemble methods, achieving high accuracy rates, particularly with K-Nearest Neighbors (KNN). Despite their complexities and challenges, advanced control methods like Model Predictive Control (MPC) and Reinforcement Learning (RL) are also explored. Overall, the study highlights the potential of integrating advanced data analysis and predictive modeling to enhance building energy management, promoting more sustainable and environmentally friendly practices.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Dongyang Zhang
PY  - 2024
DA  - 2024/09/23
TI  - Research on HVAC Occupancy Detection with ML and DL Methods
BT  - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)
PB  - Atlantis Press
SP  - 656
EP  - 666
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-512-6_69
DO  - 10.2991/978-94-6463-512-6_69
ID  - Zhang2024
ER  -