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Deep Learning for Occupancy Detection by Prakhar is a document available to read on EtoBox.

What is Deep Learning for Occupancy Detection about?

This paper presents a vision-based deep learning approach for detecting and predicting occupancy heat emissions to optimize HVAC systems in buildings. By utilizing a convolutional neural network, the model achieves an average detection accuracy of 80.62% for various occupant activities, enabling real-time adjustments to energy loads based on occupancy patterns. The findings suggest that this method can enhance energy efficiency and indoor environmental quality compared to traditional static occupancy profil

Author

Prakhar

Language

EN