The federal communications commission (FCC) has predicted that, under the current regulatory environment, a spectrum shortage may be faced in the near future. This impending spectrum shortage is in part due to a rapidly increasing demand for wireless services and in part due to inefficient usage of currently licensed bands. A new paradigm pertaining to wireless spectrum allocation, known as cognitive radio (CR), has been proposed as a potential solution to this problem. This dissertation seeks to contribute to research in the field of CR through an investigation into the effect that a primary user (PU) channel occupancy model will have on the performance of a secondary user (SU) in a CR network. The model assumes that PU channel occupancy can be described as a binary process and a two state Hidden Markov Model (HMM) was thus chosen for this investigation. Traditional algorithms for training the model were compared with certain evolutionary-based training algorithms in terms of their resulting prediction accuracy and computational complexity. The performance of this model is important since it provides SUs with a basis for channel switching and future channel allocations. A CR simulation platform was developed and the results gained illustrated the effect that the model had on channel switching and the subsequently achievable performance of a SU operating within a CR network. Performance with regard to achievable SU data throughput, PU disruption rate and SU power consumption, were examined for both theoretical test data as well as data obtained from real world spectrum measurements (taken in Pretoria, South Africa). The results show that a trade-off exists between the achievable SU throughput and the average PU disruption rate. Significant SU performance improvements were observed when prediction modelling was employed and it was found that the performance and complexity of the model were influenced by the algorithm employed to train it. SU performance was also affected by the length of the quick sensing interval employed. Results obtained from measured occupancy data were comparable with those obtained from theoretical occupancy data with an average percentage similarity score of 96% for prediction accuracy (using the Viterbi training algorithm), 90% for SU throughput, 83% for SU power consumption and 71% for PU disruption rate. / Dissertation (MEng)--University of Pretoria, 2012. / Electrical, Electronic and Computer Engineering / unrestricted

Heating, ventilation and air conditioning systems (HVAC) are the single largest consumer of energy in commercial and residential sectors. Minimizing its energy consumption without compromising indoor air quality (IAQ) and thermal comfort would result in environmental and financial benefits. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a zone excessively and result in energy waste. Previous studies showed that CO₂-based demand-controlled ventilation (DCV) methods are the most widely used strategies to determine the optimal level of supply air volume. However, conventional CO₂ mass balanced models do not yield an optimal estimation accuracy. In this study, feed-forward neural network algorithm (FFNN) was proposed to estimate the zone occupancy using CO₂ concentrations, observed occupancy data and the zone schedule. The occupancy prediction result was then utilized to optimize supply fan operation of the air handling unit (AHU) associated with the zone. IAQ and thermal comfort standards were also taken into consideration as the active constraints of this optimization. As for the validation, the experiment was carried out in an auditorium located on a university campus. The results revealed that utilizing neural network occupancy estimation model can reduce the daily ventilation energy by 74.2% when compared to the current on/off control.

The world is getting busier and more crowded each year. Due to this fact resources such as public transport, available energy, and usable space are becoming congested and require vast amounts of logistical support. As of February 2018, nearly 95% of Americans own a mobile cell phone according to the Pew Research Center. These devices are consistently broadcasting their presents to other devices. By leveraging this data to provide occupational awareness of high traffic areas such as public transit stops, buildings, etc logistic efforts can be streamline to best suit the dynamics of the population. With the rise of The Internet of Things, a scalable low-cost occupancy monitoring system can be deployed to collect this broadcasted data and present it to logistics in real time. Simple IoT devices such as the Raspberry Pi, wireless cards capable of passive monitoring, and the utilization of specialized software can provide this capability. Additionally, this combination of hardware and software can be integrated in a way to be as simple as a typical plug and play set up making system deployment quick and easy. This effort details the development and integration work done to deliver a working product acting as a foundation to build upon. Machine learning algorithms such as k-Nearest-Neighbors were also developed to estimate a mobile device's approximate location inside a building.

La presente investigación tuvo como objetivo analizar el impacto que generó el ingreso de Airbnb al mercado limeño en el sector hotelero a través de las 3 métricas más importantes para la evaluación económica de dicho sector como son los ratios de Tarifa Promedio Diaria (ADR), Tasa de Ocupación (OCC), Rentabilidad por Habitación Disponible (RevPAR). Esta investigación mixta se realizó con fines informativos y con miras a brindar un sustento válido a la industria hotelera para que pueda revisar el comportamiento de Airbnb y viceversa. Para la realización de la investigación, el primer paso fue la búsqueda, clasificación y análisis de las principales teorías de diferentes autores y temas relacionadas con nuestro tema. Seguidamente se realizó un análisis cuantitativo con la recopilación de data de la industria hotelera, de la oferta de Airbnb y de las variables de control para potenciar el modelo. Se determinó que la mejor manera de comprobar nuestra hipótesis de si el mercado hotelero fue afectado por el ingreso de Airbnb en Lima, es realizando una lectura conjunta del resultado de como la oferta de Airbnb afecta individualmente a cada uno de los ratios antes mencionados. De esta manera, complementamos la lectura del análisis cuantitativo con entrevistas realizadas a dos personas encargadas de tomar decisiones en el sector hotelero. La conclusión del análisis mixto concluyó que la hipótesis general se rechaza, es decir, que Airbnb no tiene un efecto negativo significante en el mercado hotelero. / The objective of this research was to analyze the impact generated by the Airbnb’s entry into the Lima market in the hotel sector through the 3 most important metrics for the economic evaluation in the sector, such as the Average Daily Rate (ADR), Rate Occupancy (OCC), Profitability per Available Room (RevPAR). This mixed research was made for informational purposes and for providing valid support to the hotel industry so that it can review Airbnb's behavior and reverse. To carry out the research, the first step was the search, classification and analysis of the main theories of different authors and topics related to our topic. Then, we made the quantitative analysis with the researched data from the hotel industry, Airbnb's offer and control variables to enhance the model. It was determined that the best way to check our hypothesis of whether the hotel market was affected by the entry of Airbnb in Lima, making a joint reading of the result of how the Airbnb offer individually affects each of the aforementioned ratios. In this way, it complements the reading of the quantitative analysis with interviews with two decision-makers in the hotel sector. The conclusion of the mixed analysis concluded that the general hypothesis is rejected, that is, that Airbnb does not have a significant negative effect on the hotel market. / Tesis

The design and construction of high-performance buildings have emerged as a preferred solution for reducing energy consumption and greenhouse gas emissions. However, sometimes there is a considerable gap between the design performance and the actual performance of the buildings. Post Occupancy Evaluations (POE) provide tools to quantify the performance relative to the occupant's health, well-being, and comfort. POE is getting widely accepted to obtain feedback for various parameters such as water, energy, indoor environmental quality, and occupant comfort. Key Performance Indicators (KPIs) can be derived based on the obtained feedback to determine the performance gaps. POE has evolved to be a robust scientific methodology; however, traditional methods of conducting POE have been proven time-consuming, inconsistent, and inefficient. This research aims to conceptualize the next generation of post occupancy evaluations that leverages cutting-edge technologies such as Building Information Modeling (BIM), Internet of Things based sensors (IoT), Geographic Information Systems (GIS), and digital twins. The key contributions of this research are presented in a series of manuscripts. In the first paper, the gaps in the existing POE were determined by conducting a thorough literature review. The observed gaps were classified in data collection, analysis, and visualization categories. Broader POE definition, spot measurements of parameters, and 2D plans and charts for visualization made the existing POE procedure time-consuming. Using digital twins that combine the geometric and parametric data from BIM models and built-environment data from GIS and sensor measurements were recommended as potential solutions to address the observed gaps. The second paper explored the application of BIM-IoT-GIS integration to conduct POE. Use case scenarios were developed to derive system requirements to host the BIM-IoT-GIS-integrated POE. Four sequential tests were conducted to integrate a BIM model from Revit and sensors' data from Excel with ArcGIS pro that contained the surrounding environment data. Based on lessons learned from the tests, an optimized workflow was recommended that can be used across a variety of projects. The third paper used the BIM-IoT-GIS-integration concept to create a holistic proof of concept for digital-twin-enabled POE. The proof of concept was validated by conducting a digital-twin-based POE on the STTC building on the Red River College campus in Winnipeg. The indoor thermal comfort was visualized within the STTC digital twin developed in ArcGIS Pro. The preliminary energy consumption analysis concluded that the STTC buildings' average energy savings were approximately 70,000 KWH/year. The potential users for digital-twin-enabled POE were presented with a comparison of iv existing POE and digital-twin-based POE over a survey and a focus group discussion. Based on opinion-based feedback, the conclusion can be made that digital twins improve the overall efficiency of POE. The fourth paper recommended the digital-twin-enabled POE procedure for UVic's engineering expansion project. It established the semantics for POE, followed by a digital twin execution plan that can be used for developing a digital twin during each phase (from planning to operations) of the project. Furthermore, the benefits of the digital-twin-enabled POE procedure were demonstrated by comparison with the existing POE procedure relative to the project phases. This study concluded that conducting the POE on the UVic ECS expansion project will enable the researchers to determine the effectiveness of sustainable features by comparing the performance of existing and proposed facilities. In conclusion, BIM-IoT-GIS-integrated digital twins address the limitations of data collection, analysis, and visualization. These digital twins will enable multi-objective analysis and spatial-temporal visualization and provide deeper insights into the way these high-performance buildings function. / Graduate / 2023-05-24

With robots becoming increasingly common in human occupied spaces, there has been a growing body of research into the problem of socially conscious robot navigation. A robot must be able to predict and anticipate the movements of people around it in order to navigate in a way that is socially acceptable, or it may face rejection and therefore failure. Often this motion prediction is achieved using neural networks or artificial intelligence to predict the trajectories or flow of people, requiring large amounts of expensive and time-consuming real-world data collection. Therefore, many recent studies have attempted to find a way to create simulated human trajectory data. A variety of methods have been used to achieve this, the main ones being path planning algorithms and pedestrian simulators, but no study has evaluated these methods against each other and real-world data. This thesis compares the ability of two path planning algorithms (A* and RRT*) and a pedestrian simulator (PTV Vissim) to make realistic maps of dynamics. It concludes that A*-based path planners are the best choice when balancing the ability to replicate realistic people flow with the ease of generating large amounts of data.

Since COVID-19 was declared a pandemic by the World Health Organization(WHO) in March 2020, teleworking, or working from home, has been used to an increasing extent by companies and organisations all over the world. Evidence suggests that teleworking will become part of “the new normal”, why teleworking-related research will be of value in a long-term perspective. To estimate the potential for energy saving in relation to teleworking, and to identify possible measures to achieve such savings, a literature study and a retrospective case study of an office building in Gävle, Sweden, was conducted. The occupant presence during 2020 was mapped through conversations with representatives of the organisation using the offices. Data logs of energy usage in 2020, in the form of district heating and electricity, were provided by the energy supplier. The results showed that the number of permanent office workers had dropped by just over 40% around the middle of March 2020, and that the occupancy from November 2020 onwards was just over 20 % of that by the beginning of the year. The demand for heating, cooling, and ventilation in an office is the same regardless of the number of people present, which was believed to be the explanation of the lack of covariation between occupancy and district heating supply, as well as between occupancy and HVAC electrical loads. Earlier research has found that a common reason behind lack of impact from occupancy on plug loads and lighting is that equipment and lighting is turned on in office spaces with no one present. This was not the case in the present study. The study failed to identify the reason behind plug loads and lighting having poor correlation with occupancy. Further research of the matter is encouraged. Methods for improving energy efficiency in office buildings in relation to teleworking includes presence-based control strategies for HVAC systems and lighting, energy efficient behaviour, consolidating office space, and hotdesking. Due to the lack of reliable occupancy data, the study failed in quantifying the potential for energy saving in the building, regarding both district heating and electricity. The results give clear evidence of there being an energy saving potential, but not the extent of it. / Sedan covid-19 deklarerades som en pandemi av Världshälsoorganisationen WHO i mars 2020 har distansarbete tillämpats i allt högre grad av verksamheter världen över. Mycket tyder på att distansarbete kommer att bli en del av ”det nya normala”, varför studier på områden relaterade till distansarbete kommer att vara värdefulla ur energieffektiviseringsperspektiv på lång sikt. I syfte att ta reda på hur stor energibesparingspotential distansarbete kan medföra, och att identifiera åtgärdsförslag för att uppnå sådana besparingar, genomfördes en litteraturstudie samt en retrospektiv fallstudie av en kontorsbyggnad i Gävle. Personnärvaron under 2020 kartlades i samtal med representanter för den verksamhet som har kontor i byggnaden, medan uppgifter om energitillförseln, fördelad på fjärrvärme, fastighetsel och verksamhetsel, tillhandahölls av energileverantören. Det framkom att den fasta personnärvaron sjunkit med drygt 40 % i mitten av mars 2020, och att den från och med november 2020 utgjorde drygt 20 % av närvaron vid årets början. Inga samvariationer mellan energianvändning och personnärvaro observerades, och tillförseln av såväl fjärrvärme som fastighetsel och verksamhetsel var densamma vid årets slut som vid dess början. Behovet av uppvärmning, kylning och ventilation i ett kontor är detsamma oavsett hur många personer som befinner sig i det, vilket bedömdes vara orsaken till bristen på samvariationer mellan personnärvaro och fjärrvärme respektive fastighetsel. Tidigare studier har visat att en vanlig orsak till att personnärvaro har liten påverkan på verksamhetselkonsumtion är att utrustning och belysning är påslagna även i utrymmen där ingen uppehåller sig. Så var inte fallet i föreliggande studie. Studien kunde inte identifiera orsaken till att användning av verksamhetsel inte följde variationerna i personnärvaro, varför ytterligare forskning är nödvändig. Metoder för energieffektivisering i kontorsbyggnader vid distansarbete inkluderar närvarostyrd teknologi, energimedvetet beteende, minskning av totalt utnyttjat kontorsutrymme samt hotdesking. Då personnärvaron inte kunde kartläggas med tillfredsställande precision i föreliggande studie var det inte möjligt att kvantifiera byggnadens energieffektiviseringspotential, varken för fjärrvärme eller elektricitet. Studiens resultat visar tydligt att energibesparingspotential föreligger, men inte i vilket omfång.

Snowy plovers (Charadrius nivosus) are small, ground-nesting shorebirds that are a species of conservation concern throughout North America. Despite increased efforts to understand factors contributing to the decline of snowy plover, little is known about habitat selection and breeding ecology of snowy plover for the large population found in the Great Basin. We tested hypotheses concerning the occupancy and nesting success of snowy plover. First, we identified factors influencing snowy plover nest survival at Great Salt Lake, Utah. We hypothesized that snowy plover would demonstrate differences in nest survival rates across years due to differences in habitat characteristics, predator abundance, human influence, resource availability, and fluctuating water levels. We conducted nest surveys at five sites along the Great Salt Lake to locate new nests or monitor known nests until nest fate was determined. We found 608 nests between 2003, 2005-2010, and 2012. The most common cause of nest failure was predation, followed by weather, abandonment, and trampling. Nest survival estimates ranged from 4.6 -- 46.4% with considerable yearly variation. There was no correlation between researcher activity (visits to nests and trapping of adults) and nest survival. Nests in close proximity to roads had lower survival than nests far from roads. Nests located on barren mudflats also had lower survival than nests in vegetated areas or near debris. We found that nests had a higher probability of survival as they increased in incubation stage. Because nesting areas around the Great Salt Lake host some of the largest concentrations of breeding snowy plover in North America, we suggest that managers consider measures to maintain suitable nesting habitat for snowy plover. Second, we determined factors affecting snowy plover occupancy and detection probabilities in western Utah between 2011 and 2012. We hypothesized that snowy plover would be associated with spring water flows and sparsely vegetated salt flats. We made repeated visits to randomly selected survey plots recording the number of snowy plover adults and habitat characteristics within each plot. We modeled the relationship between snowy plover detection probability and habitat and environmental characteristics. The detection probability was 77% (95% CI = 64 -- 86%) and did not vary by year. There was a positive relationship between ambient temperature and detection probability. Next, we modeled the relationship between snowy plover occupancy and individual habitat characteristics including distance to water, distance to roads, land cover types, and vegetative characteristics. Snowy plover occupancy did not vary by year and was estimated at 12% (95% CI = 7 -- 21%). Occupancy was best predicted by close proximity to water, playa land cover, and minimal shrub cover. We used habitat characteristics that best predicted snowy plover occupancy to generate a predictive habitat model that can help prioritize future snowy plover surveys and guide conservation efforts.

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The present work reports a study related to the potential improvement of the energy performances of a heat pump based heating system for a Swedish single-family house. The analysis is focused on the design of new rule-based control strategies which employ perfect predictions of weather forecast and human behaviour information. In particular, the considered signals are the outdoor temperature, the solar radiation, the internal gain due to inhabitants’ activities and the Domestic Hot Water (DHW) consumption. The study is performed by means of the TRNSYS® simulation software in which the model of the heating system is implemented. More specifically, it is composed by a Ground Source Heat Pump (GSHP) unit, a stratified storage tank of three hundred litres and the building element. The performances of the developed control logics are evaluated using a degree-minute on/off controller as reference case. The results show that the improved control logics yield to an increase of the energy efficiency of the system as well as an enhancement of the indoor and DHW temperatures stability. / EffSys Expand P18: Smart Cotnrol Strategies for Heat Pump Systems