Document Type
Article
Publication Date
11-30-2021
Publication Source
European Journal of Advances in Engineering and Technology
Abstract
Heating, Ventilating, and Air Conditioning (HVAC) systems are pivotal for maintaining indoor comfort and air quality in buildings. However, inefficient HVAC operation can lead to unnecessary energy consumption and increased costs. This research explores the application of machine learning (ML) techniques for occupancy detection using environmental factors to optimize HVAC energy consumption. Analyzing a dataset comprising environmental observations like temperature, humidity, light, and CO2 levels alongside occupancy status, we evaluate various ML algorithms, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and others, for accurately predicting room occupancy. Additionally, we conduct exploratory data analysis (EDA) using box plots to visualize feature distributions and their correlation with occupancy. Our findings highlight the potential of ML-based occupancy detection to enhance HVAC system efficiency by dynamically adjusting heating, cooling, and ventilation settings based on real-time occupancy information. Through experimental evaluation on training, validation, and test datasets, we provide insights into the performance and scalability of different ML models for occupancy prediction. Finally, we discuss practical implications and future research directions for integrating ML-based occupancy detection into HVAC control systems to achieve energy savings and environmental sustainability.
ISBN/ISSN
2394-658X
Copyright
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Volume
8
Issue
11
Keywords
HVAC energy consumption, Occupancy detection, Machine learning classifiers, Environmental factors, Building automation, Energy management, Exploratory data analysis, Feature distributions, Model performance evaluation, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbors (KNN), Naive Bayes, Decision Tree, Gradient Boosting, Sustainability, Energy savings, Environmental impact, Deep learning, Building operations.
eCommons Citation
Sharma, Vibhu and Singh, Abhimanyu, "Optimizing HVAC Energy Consumption through Occupancy Detection with Machine Learning based Classifiers" (2021). Mechanical and Aerospace Engineering Graduate Student Publications. 16.
https://ecommons.udayton.edu/mee_grad_pub/16
COinS
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.