An Improved ANN Approach for Occupancy Detection of a Smart Building

Abstract

Building energy performance can be improved with a reduction in energy consumption. The heating and cooling loads of a building are important factors to consider in the field of energy conservation. It is possible to estimate energy consumption by predicting the presence of occupants in a room based on information provided by the HVAC (Heating, Ventilation, and Air Conditioning) system using standard information. Temperature, humidity, light, and CO2 levels from various sensors are taken as input parameters. In addition, the output of the network is programmed to be "0" when the building is not occupied and "1" when the building is occupied for the purpose of occupant detection. Pattern recognition using an Elman back propagation network is being proposed for occupancy detection. The data sets were used for training and testing (with the office door open and closed) the models during occupancy. The proposed ANN-based method is trained and tested and was found to be more effective, with an accuracy of 98.5% and 97.5% in cases of closed and opened doors, respectively.