Occupancy

About: Occupancy is a research topic. Over the lifetime, 2757 publications have been published within this topic receiving 68288 citations.

A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand

Abstract: Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

RESEARCH Applying occupancy estimation and modelling to the analysis of atlas data

Abstract: Aim Biological atlases are a globally widespread and effective means for documenting the distribution of numerous taxa and have been used to study many macroecological relationships. A common assumption when analysing atlas data is that species are detected perfectly (p = 1). This assumption is likely incorrect, but the application of methods to account for heterogeneous detectability ( p< 1) has been difficult to implement. We provide an application of current methods of occupancy estimation and modelling to account for imperfect detection in the analysis of atlas data. Location New York, USA. Methods We employed multiseason occupancy models substituting spatial replicates for temporally repeated surveys to examine changes in distribution of the Canada Warbler (Cardellina canadensis) using breeding bird atlases from 1980‐1985 and 2000–2005. We compared estimates from models accounting for p< 1 versus those assuming p = 1 in assessing statewide patterns of occupancy, colonization and extinction. Results We found forest cover, observer effort, information on previous detections and the sampling year were important predictors of detection. Environmental predictors of statewide occupancy dynamics were similar among models accounting for p< 1 versus those assuming p = 1. Despite these similarities, site-level estimates of occupancy from the model accounting for imperfect detection indicated 14% and 19% higher site occupancy in the 1980–1985 and 2000–2005 Atlases, respectively. In addition, relative to the model accounting for p < 1, the model assuming perfect detectability underestimated persistence and overestimated extinction between atlases. The model accounting for p< 1 had 0.3% and 7.9% higher accuracy in predicting occupancy in the 1980–1985 and 2000–2005 Atlases, respectively, than the models assuming p = 1. Main conclusions Occupancy modelling and estimation can be successfully applied to broad-scale surveys, such as atlases, that do not explicitly implement repeated visits to a survey block. Occupancy modelling allows for a more rigorous analysis of atlas data for exploring species–environmental relationships and modelling species distributions while accounting for imperfect detection.

A Field Survey of Occupancy and Air-Conditioner Use Patterns in Open Plan Offices

Abstract: This paper reports the survey results from a field monitoring study of office occupancy and air-conditioner use pattern of an office building in Seoul, Korea, from 20 July to 29 September 2009. The survey has an aim to reveal the relationship between the indoor environment conditions and occupancy; and the use patterns of air-conditioners and energy consumption in offices. The results showed that the average occupied time of the investigated offices was 15.48 h per day for normal weekdays, which was longer than expected occupancy schedules assumed for offices for the design prediction of building energy consumption. This study also presents the hourly profiles of air-conditioner use and evaluates the effect of longer occupancy period on energy consumption for air cooling in open-plan offices in Korea. The findings of this study could contribute to inform building energy simulation based on realistic office occupancy profile provided by the survey, which would therefore inform better operation of air-condi...

Validating the application of occupancy sensor networks for lighting control

Abstract: A new occupancy detection sensor network was developed, commissioned and installed in two private offices: data were collected to evaluate the utility of the sensor network for lighting control. Re...

Smart building management vs. intuitive human control—Lessons learnt from an office building in Hungary

Abstract: Smart building management and control are adopted nowadays to achieve zero-net energy use in buildings. However, without considering the human dimension, technologies alone do not necessarily guarantee high performance in buildings. An office building was designed and built according to state-of-the-art design and energy management principles in 2008. Despite the expectations of high performance, the owner was facing high utility bills and low user comfort in the building located in Budapest, Hungary. The objective of the project was to evaluate the energy performance and comfort indices of the building, to identify the causes of malfunction and to elaborate a comprehensive energy concept. Firstly, current building conditions and operation parameters were evaluated. Our investigation found that the state-of-the-art building management system was in good conditions but it was operated by building operators and occupants who are not aware of the building management practice. The energy consumption patterns of the building were simulated with energy modelling software. The baseline model was calibrated to annual measured energy consumption, using actual occupant behaviour and presence, based on results of self-reported surveys, occupancy sensors and fan-coil usage data. Realistic occupant behaviour models can capture diversity of occupant behaviour and better represent the real energy use of the building. This way our findings and the effect of our proposed improvements could be more reliable. As part of our final comprehensive energy concept, we proposed intervention measures that would increase indoor thermal comfort and decrease energy consumption of the building. A parametric study was carried out to evaluate and quantify energy, comfort and return on investment of each measure. It was found that in the best case the building could save 23% of annual energy use. Future work includes the follow-up of: occupant reactions to intervention measures, the realized energy savings, the measurement of occupant satisfaction and behavioural changes.