Document Type

Article

Publication Date

11-30-2021

Publication Source

European Journal of Advances in Engineering and Technology

Abstract

This study investigates the utilization of Artificial Neural Networks (ANNs) for occupancy detection to optimize Heating, Ventilating, and Air Conditioning (HVAC) energy consumption. Leveraging environmental factors such as temperature, humidity, light, and CO2 levels, alongside occupancy status, various ANN architectures' efficacy in accurately predicting room occupancy is evaluated. The research involves comprehensive data analysis, including exploratory data analysis (EDA) and model evaluation on training, validation, and test datasets. The study demonstrates the potential of ANN-based occupancy detection in enhancing HVAC system efficiency by dynamically adjusting heating, cooling, and ventilation settings based on real-time occupancy information. Practical implications and future research directions for integrating ANN-based occupancy detection into HVAC control systems to achieve energy savings and environmental sustainability are discussed.

ISBN/ISSN

2394-658X

Comments

The document available for download is the published version, provided in compliance with the publisher's open-access policy. Permission documentation is on file. DOI: https://doi.org/10.5281/zenodo.11103770

Volume

8

Issue

11

Keywords

HVAC energy efficiency, Artificial Neural Networks (ANNs), occupancy detection, environmental factors, temperature, humidity, light, CO2 levels, room occupancy prediction, data analysis, Exploratory data analysis (EDA), model evaluation, training, validation, and test datasets, energy savings, environmental sustainability, building automation, optimization strategies

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