Occupancy

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

The combined effects of land‐use legacies and novel fire regimes on bird distributions in the Mediterranean

Abstract: Aim We investigate first whether fire regimes resulting from the combination of climate change and fire-fighting policy may affect species distributions in Mediterranean landscapes, and second to what extent distributional dynamics may be constrained by the spatial legacy of historical land use. Location Catalonia (north-eastern Spain). Methods We modelled the distributional responses of 64 forest and openhabitat bird species to nine fire-regime scenarios, defined by combining different levels of climate change and fire suppression efficiency. A fire-succession model was used to stochastically simulate land-cover changes between 2000 and 2050 under these scenarios. We used species distribution models to predict habitat suitability and occupancy dynamics under either no dispersal or full dispersal assumptions. Results Under many simulated scenarios, the succession from shrubland to forest dominated over the creation of new low-vegetation areas derived from wildfires. Consequently, open-habitat specialists were the group most affected by losses of suitable habitat. Fire regimes obtained under scenarios including high fire suppression efficiency resulted in a larger number of bird species experiencing reductions in their distribution area. Main conclusions Anthropogenic factors, such as historical land-use change and fire suppression, can drive regional distribution dynamics in directions opposite to those expected from climatic trends. This raises the question of what drivers and interactions should be given priority in the prediction of biodiversity responses to global change at the regional scale.

Occupancy Estimation Using Thermal Imaging Sensors and Machine Learning Algorithms

Abstract: Occupancy estimation has a broad range of applications in security, surveillance, traffic and resource management in smart building environments. Low-resolution thermal imaging sensors can be used for real-time non-intrusive occupancy estimation. Such sensors have a resolution that is too low to identify occupants, but it may provide sufficient data for real-time occupancy estimation. In this paper, we present a systematic study of three thermal imaging sensors with different resolutions, with a focus on sensor characterization, estimation algorithms, and comparative analysis of occupancy estimation performance. A unified processing algorithms pipeline for occupancy estimation is presented and the performance of three sensors are compared side-by-side. A number of specific algorithms are proposed for pre-processing of sensor data, feature extraction, and fine-tuning of the occupancy estimation algorithms. Our results show that it is possible to achieve about 99% accuracy for occupancy estimation with our proposed approach, which might be sufficient for many practical smart building applications.

Comparing occupancy models and data mining approaches for regular occupancy prediction in commercial buildings

Abstract: Occupancy information can help us to achieve high energy-efficient buildings. Previous works mainly focus on predicting the presence and absence of occupants in homes or single person offices. We attempt to predict regular occupancy level in a commercial building deployment scenario. The occupancy prediction models can be divided into two categories of occupancy models and data mining approaches. For the occupancy models, we shall investigate the efficiencies of two widely used multi-occupant models, that is, inhomogeneous Markov chain and multivariate Gaussian. For the data mining approaches, we propose the application of autoregressive integrated moving average, artificial neural network and support vector regression. Experiments have been conducted using actual occupancy data under four different prediction horizons, that is, 15 min, 30 min, 1 and 2 h. The results demonstrated a guideline in how to choose a proper method for the prediction of occupancy in commercial buildings under different prediction...

Analyzing occupancy profiles from a lighting controls field study

Abstract: Despite a number of published studies on the effectiveness of lighting controls in buildings, only one US study examines the occupancy patterns of building occupants. Occupancy profiles allow one to determine, for example, the probability that an office is occupied for each hour of the workday. Occupancy profiles are useful for many purposes including: (1) predicting the effectiveness of occupancy sensors for reducing peak demand, (2) evaluating the impact of human activity on building lighting and other electric loads and (3) providing lighting equipment manufacturers with detailed lighting operation data to help evaluate the impact of advanced lighting controls on equipment life. In this paper, we examine the occupancy profiles for 35 single person offices at a large office building in San Francisco and analyze the data to obtain average occupancy as a function of time of day. In addition, we analyzed the data to identify how the use of occupancy sensors may affect switching cycles and lamp life.