EMBEDDED CONTROLLER FOR ENERGY EFFICIENT OCCUPANCY MONITORING

Abstract

Air conditioning systems are the important part for providing ambient atmosphere inside buildings, for that building energy management systems (BEMS) are used. Ambient atmosphere means temperature control, humidity control, electronic equipment’s control, etc. The system consists of an embedded processor with an Internet of things (IoT) platform integrated with IR sensor and camera systems. The IoT devices have several sensors for measuring various parameters like temperature, humidity, inlet air coming from the air conditioning (AC) duct. The Random Neural Network (RNN) based occupancy calculation technique integrates with cloud computing environment, which estimate the occupants in the cabin and provides this information to the control station. The control station uploads the data on a web page to collect the trained RNN values for sensor nodes. The control station contains RNN algorithm for occupancy detection to manage the temperature and vents of the system. The HVAC system of the building uses 29.50% less energy with RNN based controller than other existing techniques.

Keywords: Cloud computing, intelligent sensors, occupancy estimation, random neural networks, WSN

Cite this Article
Nithya V.S., S. Suresh Babu. Embedded Controller for Energy Efficient Occupancy Monitoring. Journal of Software Engineering Tools & Technology Trends. 2018; 5(1): 6–11p.