Utilization of the LMS Algorithm to Filter the Predicted Course by Means of Neural Networks for Monitoring the Occupancy of Rooms in an Intelligent Administrative Building

Abstract

For monitoring the occupancy of individual rooms in an intelligent administrative building (IAB), a wide variety of sensors can be used, by means of which the presence of a person in the monitored area can be determined. For a larger administrative-type building, installation of additional sensors means considerable investment costs. As the present-day standard, temperature or also humidity sensors are installed in individual IAB rooms. The paper describes the proposed method for the determination of occupancy of the monitored area by means of prediction of the course of CO2(ppm) from the measured values of humidity rH(%), indoor temperature Ti(°C) and outdoor temperature To(°C), using the gradient algorithm of back-propagation of error for adaptation of the multilayer feedforward Artificial Neural Network (ANN) in the IAB areas with utilization of the Bayesian regularization method (BRM) to obtain information on the occupancy of individual rooms. The LMS algorithm was used to filter the predicted course in order to determine the occupancy of the monitored areas more precisely. The advantage of the proposed method is the utilization of common operating sensors to obtain information on the state of operational-technical functions in the IAB for the purpose of optimum control of the operational-technical functions of the IAB on the basis of predictable needs of persons using the IAB areas.