A design model for building occupancy detection using sensor fusion

Abstract

Building occupancy sensing is useful for control of building services such as lighting and ventilation, enabling energy savings, whilst maintaining a comfortable environment. However, a precise and reliable measurement of occupancy still remains difficult. Existing technologies are plagued with a number of issues ranging from unreliable data, maintaining privacy, sensor drift, change of use, and short-term financial pressures, including low quality parts and insufficient commissioning. A major performance barrier is currently the fitness to purpose, or otherwise of sensing technologies used. Sensor fusion techniques offer a way to make up for this, aiming to more reliably determine occupancy using a range of different indoor climatic variables. Over the last decade, artificial intelligence (AI) techniques have found some application for building controls, and can also be applied to occupancy estimation. We describe a novel methodology for building occupancy detection using a sensor fusion model based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm. The system monitors indoor climatic variables, indoor events and energy data obtained from a non-domestic building to infer occupancy patterns.