Advanced Occupancy Measurement Using Sensor Fusion

With roughly about half of the energy used in buildings attributed to Heating, Ventilation, and Air conditioning (HVAC) systems, there is clearly great potential for energy saving through improved building operations. Accurate knowledge of localised and real-time occupancy numbers can have compelling control applications for HVAC systems. However, existing technologies applied for building occupancy measurements are limited, such that a precise and reliable occupant count is difficult to obtain. For example, passive infrared (PIR) sensors commonly used for occupancy sensing in lighting control applications cannot differentiate between occupants grouped together, video sensing is often limited by privacy concerns, atmospheric gas sensors (such as CO2 sensors) may be affected by the presence of electromagnetic (EMI) interference, and may not show clear links between occupancy and sensor values. Past studies have indicated the need for a heterogeneous multi-sensory fusion approach for occupancy detection to address the short-comings of existing occupancy detection systems. The aim of this research is to develop an advanced instrumentation strategy to monitor occupancy levels in non-domestic buildings, whilst facilitating the lowering of energy use and also maintaining an acceptable indoor climate. Accordingly, a novel multi-sensor based approach for occupancy detection in open-plan office spaces is proposed. The approach combined information from various low-cost and non-intrusive indoor environmental sensors, with the aim to merge advantages of various sensors, whilst minimising their weaknesses. The proposed approach offered the potential for explicit information indicating occupancy levels to be captured. The proposed occupancy monitoring strategy has two main components; hardware system implementation and data processing. The hardware system implementation included a custom made sound sensor and refinement of CO2 sensors for EMI mitigation. Two test beds were designed and implemented for supporting the research studies, including proof-of-concept, and experimental studies. Data processing was carried out in several stages with the ultimate goal being to detect occupancy levels. Firstly, interested features were extracted from all sensory data collected, and then a symmetrical uncertainty analysis was applied to determine the predictive strength of individual sensor features. Thirdly, a candidate features subset was determined using a genetic based search. Finally, a back-propagation neural network model was adopted to fuse candidate multi-sensory features for estimation of occupancy levels. Several test cases were implemented to demonstrate and evaluate the effectiveness and feasibility of the proposed occupancy detection approach. Results have shown the potential of the proposed heterogeneous multi-sensor fusion based approach as an advanced strategy for the development of reliable occupancy detection systems in open-plan office buildings, which can be capable of facilitating improved control of building services. In summary, the proposed approach has the potential to: (1) Detect occupancy levels with an accuracy reaching 84.59% during occupied instances (2) capable of maintaining average occupancy detection accuracy of 61.01%, in the event of sensor failure or drop-off (such as CO2 sensors drop-off), (3) capable of utilising just sound and motion sensors for occupancy levels monitoring in a naturally ventilated space, (4) capable of facilitating potential daily energy savings reaching 53%, if implemented for occupancy-driven ventilation control

Improved occupancy monitoring in non-domestic buildings

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Measuring occupancy can facilitate energy efficiency in non-domestic buildings, when control systems are able to adjust heating and cooling based on demand rather than fixed schedules. The variable “occupancy profile” itself is rarely considered as a control system parameter in building energy management systems (BEMS), and this is largely because reliably measuring occupancy in the past has been too difficult, expensive, or a mixture of both. Occupancy detection is possible using e.g. CO2 sensors, passive infra-red (PIR) detectors, which can provide a basic trigger for services, but the actual occupancy count, and therefore the expected load on building services, requires a step change in instrumentation. Advanced occupancy sensors developed from a heterogeneous multisensory fusion strategy offer this, improving control system performance, e.g. turning off services out of hours, and not over-ventilating, saving energy, while not under-ventilating during occupancy, benefitting comfort and health. While this is the case, there is a shortage of any systematic methodology for developing robust and reliable occupancy monitoring systems from heterogeneous multi-sensory sources. In this paper we describe an innovative sensor fusion approach utilising symmetrical uncertainty (SU) analysis and a genetic based feature selection for building occupancy estimation

Advanced Occupancy Sensing for Energy Efficiency in Office Buildings

Control systems for Heating, Ventilation and Air-conditioning (HVAC) in non-domestic buildings often operate to fixed schedules, assuming maximum occupancy during business hours. Since lower occupancies usually mean less demand for HVAC, energy savings could be made. Air quality sensing, often combined with temperature sensing, has performed sufficiently in the past for this if maintained properly, although sensor and control failures may increase energy use by as much as 50%. As energy costs increase, building controls must meet increasingly stringent environmental requirements, increases in building services complexity, and reduced commissioning time, all placing ever higher demands on sensing, with a standing requirement to improve reliability. Sensor fusion offers performance and resilience to meet these demands, while cost and privacy are key factors which are also met. This paper describes a neural network approach to sensor fusion for occupancy estimation. Feature selection was carried out using symmetrical uncertainty analysis, while fusion of sensor features used a back-propagation neural network, with occupant count accuracy exceeding 74%