Improving Indoor occupancy estimation using a hybrid CNN-LSTM approach

Title

Improving Indoor occupancy estimation using a hybrid CNN-LSTM approach

Creator

Ramanujam E.; Sharma A.; Hussian J.J.; Perumal T.

Description

Indoor Air Quality (IAQ) monitoring has been a significant research domain in energy conservation. Many energy resources are required to maintain the IAQ using airconditioning or other ventilation systems. Currently, the research works highly optimize an on-demand driven energy usage depending on the occupant present inside the building. In the last decade, numerous research works have evolved for such an optimization by installing sensors and predicting occupants using machine learning techniques. This research fails to deploy non-intrusive sensors and appropriate machine learning algorithms to predict the occupancy count. Advancement in neural network techniques termed deep learning has made significant performance in recognition and cognitive tasks. Thus, this paper proposes a hybrid deep learning model that stacks the convolutional neural network (CNN) and long short term memory (LSTM) to improve the prediction rate of the occupancy count. Experimentation has been carried out in real-time multivariate sensor data for the occupancy estimation and evaluated the performance in terms of accuracy, RMSE, MAPE, and coefficients of determinants. 2022 IEEE.

Source

2022 International Conference on Intelligent Controller and Computing for Smart Power, ICICCSP 2022

Date

2022-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

air quality; CNN; Deep learning; IAQ; LSTM; occupancy estimation

Coverage

Ramanujam E., School of Engineering and Technology, Christ (Deemed to Be) University, Department of Computer Science and Engineering, Bengaluru, India; Sharma A., School of Engineering and Technology, Christ (Deemed to Be) University, Department of Computer Science and Engineering, Bengaluru, India; Hussian J.J., School of Engineering and Technology, Christ (Deemed to Be) University, Department of Computer Science and Engineering, Bengaluru, India; Perumal T., Universiti Putra, Department of Computer Science, Serdany, Selangor, Malaysia

Rights

Restricted Access

Relation

ISBN: 978-166547258-6

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICICCSP53532.2022.9862328

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