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.

Predicting project duration using a coupled artificial neural network and Taguchi method approach

“…While traditional tools like GANTT and PERT diagrams are widely used, they often fall short in supporting the iterative nature of design processes, where tasks may require revisiting to refine previous steps (Li, 2013) (Larbi et al, 2022). To address these challenges, several models have been developed to represent iterative design processes, with notable studies exploring sequential, parallel, or coupled methodologies (Ray et al, 2023). The iterative approach has proven instrumental in analyzing and optimizing product development timelines.…”

“…Concurrently, studies have leveraged data from neural networks to analyze recovery operations' impact on project cost, time overruns, and contractual claims, enhancing decision-making frameworks for improving project outcomes (Mukrimaa et al, 2021). Recent research in smart buildings has underscored the efficacy of Artificial Neural Networks (ANN) in optimizing energy consumption through intelligent occupancy detection (Ray et al, 2023). This method, highlighted by a study achieving accuracies of 98.5% and 97.5% for closed and opened door scenarios respectively, holds promise for enhancing energy management strategies in building automation systems.…”