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European Journal of Advances in Engineering and Technology


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.




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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.