ANN-based occupancy detection for energy efficient HVAC control: a case study.
P Adhikary, Sanghamitra Bandyopadhyay, Amrita Mazumdar
Abstract
P Adhikary, Sanghamitra Bandyopadhyay, Amrita Mazumdar
Abstract
Current building climate control systems often rely on pre-determined maximum occupancy numbers coupled with temperature sensor data to regulate heating, ventilation, and air conditioning (HVAC). However, rooms and zones in a building are not always fully occupied. Real-time occupancy information can potentially be used to reduce energy consumption. The paper proposes an ANN-based occupancy detection solution to address the need for real-time in-building occupancy information. The proposed solution can track real-time location of tagged occupants. Based on the findings, detailed and operable strategies for optimizing HVAC related building energy consumption by using occupancy information are proposed. Good agreement was found between the simulated results and obtained project data.
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Current building climate control systems often rely on pre-determined maximum occupancy numbers coupled with temperature sensor data to regulate heating, ventilation, and air conditioning (HVAC). However, rooms and zones in a building are not always fully occupied. Real-time occupancy information can potentially be used to reduce energy consumption. The paper proposes an ANN-based occupancy detection solution to address the need for real-time in-building occupancy information. The proposed solution can track real-time location of tagged occupants. Based on the findings, detailed and operable strategies for optimizing HVAC related building energy consumption by using occupancy information are proposed. Good agreement was found between the simulated results and obtained project data.
Key concepts: Occupancy, HVAC, Air conditioning, Energy consumption, Computer science, Ventilation (architecture), Real-time computing, Energy (signal processing)