scholarly journals Species niches, not traits, determine abundance and occupancy patterns: A multi‐site synthesis

2019 ◽  
Vol 29 (2) ◽  
pp. 295-308
Author(s):  
Nicholas A. C. Marino ◽  
Régis Céréghino ◽  
Benjamin Gilbert ◽  
Jana S. Petermann ◽  
Diane S. Srivastava ◽  
...  
Keyword(s):  
Occupancy Patterns

scholarly journals Online Implementation of a Soft Actor-Critic Agent to Enhance Indoor Temperature Control and Energy Efficiency in Buildings

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 997
Author(s):  
Davide Coraci ◽  
Silvio Brandi ◽  
Marco Savino Piscitelli ◽  
Alfonso Capozzoli
Keyword(s):  
Energy Consumption ◽  
Temperature Control ◽  
Weather Conditions ◽  
Heating System ◽  
Building Envelope ◽  
Indoor Temperature ◽  
Model Free ◽  
Occupancy Patterns ◽  
Online Implementation ◽  
Indoor Temperature Control

Recently, a growing interest has been observed in HVAC control systems based on Artificial Intelligence, to improve comfort conditions while avoiding unnecessary energy consumption. In this work, a model-free algorithm belonging to the Deep Reinforcement Learning (DRL) class, Soft Actor-Critic, was implemented to control the supply water temperature to radiant terminal units of a heating system serving an office building. The controller was trained online, and a preliminary sensitivity analysis on hyperparameters was performed to assess their influence on the agent performance. The DRL agent with the best performance was compared to a rule-based controller assumed as a baseline during a three-month heating season. The DRL controller outperformed the baseline after two weeks of deployment, with an overall performance improvement related to control of indoor temperature conditions. Moreover, the adaptability of the DRL agent was tested for various control scenarios, simulating changes of external weather conditions, indoor temperature setpoint, building envelope features and occupancy patterns. The agent dynamically deployed, despite a slight increase in energy consumption, led to an improvement of indoor temperature control, reducing the cumulative sum of temperature violations on average for all scenarios by 75% and 48% compared to the baseline and statically deployed agent respectively.

scholarly journals Habitat Partitioning and Overlap by Large Lacertid Lizards in Southern Europe

Diversity ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 155
Author(s):  
Daniel Escoriza ◽  
Félix Amat
Keyword(s):  
Related Species ◽  
Vegetation Cover ◽  
Western Europe ◽  
Phylogenetic Analyses ◽  
Niche Partitioning ◽  
Probabilistic Methods ◽  
Climate Niche ◽  
Rich Diversity ◽  
Occupancy Patterns ◽  
Lacertid Lizards

South-western Europe has a rich diversity of lacertid lizards. In this study, we evaluated the occupancy patterns and niche segregation of five species of lacertids, focusing on large-bodied species (i.e., adults having >75 mm snout-vent length) that occur in south-western Europe (Italian to the Iberian Peninsula). We characterized the niches occupied by these species based on climate and vegetation cover properties. We expected some commonality among phylogenetically related species, but also patterns of habitat segregation mitigating competition between ecologically equivalent species. We used multivariate ordination and probabilistic methods to describe the occupancy patterns and evaluated niche evolution through phylogenetic analyses. Our results showed climate niche partitioning, but with a wide overlap in transitional zones, where segregation is maintained by species-specific responses to the vegetation cover. The analyses also showed that phylogenetically related species tend to share large parts of their habitat niches. The occurrence of independent evolutionary lineages contributed to the regional species richness favored by a long history of niche divergence.

Social Accounts and Planning for Changes in Urban Housing Markets

1978 ◽  
Vol 10 (3) ◽  
pp. 247-266 ◽  
Author(s):  
J W Byler ◽  
S Gale
Keyword(s):  
Housing Market ◽  
Stochastic Models ◽  
Housing Markets ◽  
Urban Housing ◽  
Dwelling Unit ◽  
Urban Populations ◽  
Social Accounts ◽  
Dynamic Matching ◽  
Matching Process ◽  
Occupancy Patterns

A conception of the housing market as a lagged, dynamic matching process is presented as an alternative to the conventional microeconomic formulation. Various components of changes in occupancy patterns are identified, in a general multidimensional accounting framework, as a means for the structuring of observations of household and dwelling-unit characteristics of urban populations. Parameters for several stochastic models of housing-market phenomena are derived from the account-based representation. Finally, potential planning applications of these accounting frameworks are explored together with conditions for their adoption.

scholarly journals Carnivore distributions across chaparral habitats exposed to wildfire and rural housing in southern California

2014 ◽  
Vol 23 (4) ◽  
pp. 591 ◽  
Author(s):  
P. A. Schuette ◽  
J. E. Diffendorfer ◽  
D. H. Deutschman ◽  
S. Tremor ◽  
W. Spencer
Keyword(s):  
Southern California ◽  
Coastal Sage Scrub ◽  
Lynx Rufus ◽  
Management Policy ◽  
Native Plant ◽  
Anthropogenic Pressures ◽  
Gray Fox ◽  
Striped Skunk ◽  
Occupancy Patterns ◽  
Animal Communities

Chaparral and coastal sage scrub habitats in southern California support biologically diverse plant and animal communities. However, native plant and animal species within these shrubland systems are increasingly exposed to human-caused wildfires and an expansion of the human–wildland interface. Few data exist to evaluate the effects of fire and anthropogenic pressures on plant and animal communities found in these environments. This is particularly true for carnivore communities. To address this knowledge gap, we collected detection–non-detection data with motion-sensor cameras and track plots to measure carnivore occupancy patterns following a large, human-caused wildfire (1134km2) in eastern San Diego County, California, USA, in 2003. Our focal species set included coyote (Canis latrans), gray fox (Urocyon cinereoargenteus), bobcat (Lynx rufus) and striped skunk (Mephitis mephitis). We evaluated the influence on species occupancies of the burned environment (burn edge, burn interior and unburned areas), proximity of rural homes, distance to riparian area and elevation. Gray fox occupancies were the highest overall, followed by striped skunk, coyote and bobcat. The three species considered as habitat and foraging generalists (gray fox, coyote, striped skunk) were common in all conditions. Occupancy patterns were consistent through time for all species except coyote, whose occupancies increased through time. In addition, environmental and anthropogenic variables had weak effects on all four species, and these responses were species-specific. Our results helped to describe a carnivore community exposed to frequent fire and rural human residences, and provide baseline data to inform fire management policy and wildlife management strategies in similar fire-prone ecosystems.

scholarly journals Mapping similarities in temporal parking occupancy behavior based on city-wide parking meter data

2018 ◽  
Vol 1 ◽  
pp. 1-5
Author(s):  
Fabian Bock ◽  
Karen Xia ◽  
Monika Sester
Keyword(s):  
Similarity Measure ◽  
Historical Data ◽  
Parking Space ◽  
Number Of Clusters ◽  
Occupancy Patterns ◽  
Silhouette Index ◽  
The Mean ◽  
Occupancy Behavior ◽  
Maximum Occupancy ◽  
Repetitive Pattern

The search for a parking space is a severe and stressful problem for drivers in many cities. The provision of maps with parking space occupancy information assists drivers in avoiding the most crowded roads at certain times. Since parking occupancy reveals a repetitive pattern per day and per week, typical parking occupancy patterns can be extracted from historical data.<br> In this paper, we analyze city-wide parking meter data from Hannover, Germany, for a full year. We describe an approach of clustering these parking meters to reduce the complexity of this parking occupancy information and to reveal areas with similar parking behavior. The parking occupancy at every parking meter is derived from a timestamp of ticket payment and the validity period of the parking tickets. The similarity of the parking meters is computed as the mean-squared deviation of the average daily patterns in parking occupancy at the parking meters. Based on this similarity measure, a hierarchical clustering is applied. The number of clusters is determined with the Davies-Bouldin Index and the Silhouette Index.<br> Results show that, after extensive data cleansing, the clustering leads to three clusters representing typical parking occupancy day patterns. Those clusters differ mainly in the hour of the maximum occupancy. In addition, the lo-cations of parking meter clusters, computed only based on temporal similarity, also show clear spatial distinctions from other clusters.

scholarly journals Towards a Living Lab for Enhanced Thermal Comfort and Air Quality: Analyses of Standard Occupancy, Weather Extremes, and COVID-19 Pandemic

2021 ◽  
Vol 9 ◽  
Author(s):  
Giulia Ulpiani ◽  
Negin Nazarian ◽  
Fuyu Zhang ◽  
Christopher J. Pettit
Keyword(s):  
Environmental Quality ◽  
Low Cost ◽  
Design Guidelines ◽  
Weather Extremes ◽  
Fine Grained ◽  
Occupancy Patterns ◽  
Living Lab ◽  
Spatial Coverage ◽  
Educational Buildings ◽  
Extreme Variability

Maintaining indoor environmental (IEQ) quality is a key priority in educational buildings. However, most studies rely on outdoor measurements or evaluate limited spatial coverage and time periods that focus on standard occupancy and environmental conditions which makes it hard to establish causality and resilience limits. To address this, a fine-grained, low-cost, multi-parameter IOT sensor network was deployed to fully depict the spatial heterogeneity and temporal variability of environmental quality in an educational building in Sydney. The building was particularly selected as it represents a multi-use university facility that relies on passive ventilation strategies, and therefore suitable for establishing a living lab for integrating innovative IoT sensing technologies. IEQ analyses focused on 15 months of measurements, spanning standard occupancy of the building as well as the Black Summer bushfires in 2019, and the COVID-19 lockdown. The role of room characteristics, room use, season, weather extremes, and occupancy levels were disclosed via statistical analysis including mutual information analysis of linear and non-linear correlations and used to generate site-specific re-design guidelines. Overall, we found that 1) passive ventilation systems based on manual interventions are most likely associated with sub-optimum environmental quality and extreme variability linked to occupancy patterns, 2) normally closed environments tend to get very unhealthy under periods of extreme pollution and intermittent/protracted disuse, 3) the elevation and floor level in addition to room use were found to be significant conditional variables in determining heat and pollutants accumulation, presumably due to the synergy between local sources and vertical transport mechanisms. Most IEQ inefficiencies and health threats could be likely mitigated by implementing automated controls and smart logics to maintain adequate cross ventilation, prioritizing building airtightness improvement, and appropriate filtration techniques. This study supports the need for continuous and capillary monitoring of different occupied spaces in educational buildings to compensate for less perceivable threats, identify the room for improvement, and move towards healthy and future-proof learning environments.

ANALYSES ON OCCUPANT PATTERNS AND ENERGY CONSUMPTION IN RESIDENTIAL BUILDINGS INCLUDING THE COVID-19 PANDEMIC

2021 ◽  
Author(s):  
B. Manav ◽  
E. Kaymaz
Keyword(s):  
Energy Demand ◽  
Residential Buildings ◽  
Well Being ◽  
Lighting Design ◽  
Occupancy Patterns ◽  
Lighting Energy ◽  
And Performance ◽  
Mental Well Being

In the last years, as a result of environmental concerns, changes in lifestyle during the COVID-19 crisis, the role of healthy buildings in addition to the main lighting design principles are highlighted. Therefore, today’s lighting design issues include social well-being, mental well-being, and physical well-being more than we discussed in the last century. Hence, we are familiar with occupant-centric and performance-based metrics for residential and non-domestic buildings. The study analyses the extended occupancy patterns, daylight availability, and annual lighting energy demand through a case study in Bursa, Turkey including the COVID-19 pandemic scenario.

BLEMAT: Data Analytics and Machine Learning for Smart Building Occupancy Detection and Prediction

2019 ◽  
Vol 28 (06) ◽  
pp. 1960005 ◽  
Author(s):  
Saša Pešić ◽  
Milenko Tošić ◽  
Ognjen Iković ◽  
Miloš Radovanović ◽  
Mirjana Ivanović ◽  
...  
Keyword(s):  
Data Analysis ◽  
Experimental Validation ◽  
Building Blocks ◽  
Management Systems ◽  
Smart Building ◽  
Occupancy Detection ◽  
Occupancy Patterns ◽  
Building Management ◽  
Building Occupancy ◽  
Building Management Systems

Running costs of buildings represent a significant outlay for all businesses, thus finding a way to run facilities as efficiently as possible is vital. IoT-enabled Building Management Systems provide means for process and resource usage automation leading to overall efficiency improvements. Inferring spatial and temporal occupancy in all its forms (binary, numerical or continuous) is one of the key contextual inputs required for smart building management systems. In this work, we showcase design, implementation and experimental validation of a smart building occupancy detection and forecasting solution. The presented solution comprises three main building blocks: (1) A fog computing indoor positioning system (BLEMAT — Bluetooth Low Energy Microlocation Asset Tracking) which, combined with wireless access network monitoring processes, produces indoor location information in a semi-unsupervised manner; (2) Data analysis and pattern searching pipelines responsible for fusing data coming from different smart building and networking systems and deriving information on temporal and spatial occupancy patterns; (3) Long short-term memory (LSTM) neural networks trained to predict occupancy patterns in different areas of a smart building. Data analysis and neural network training are conducted on real-world smart building dataset which authors provide in public online repository. Experimental validation confirms that the proposed solution can provide actionable occupancy detection and prediction information, required by smart building management systems.

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