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Thesis (MTech (Mech. Eng.))--Peninsula Technikon, 1999. / Word processed copy. Summary in English. Includes bibliographical references (leaves 72-75). Also available online.

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Ubiquitous computing describes the aspirations of including information systems into the fabric of our daily lives. The emphasis of the user should be on what task needs to be achieved rather than how the system will do it. The interface of applications that are integrated into the home fabric needs to be delicate in the sense that it should give the feeling of being a part of the home rather than a piece of technology. The human computer interaction/interface needs to be cohesive with the environment people live in so as to maximise the interaction possibilities. Photographs are a very affective and efficient way of connecting people to each other. We should not forget the social role photographs have to play. The most important aspect of the picture is the story that it is trying to convey, the people in it, or the place it was taken. Emotions are a very private part of our personal life and how one displays it reflects one’s lifestyle. In recent years increasing efforts have been put into preserving emotions in photos particularly with the advent of digital cameras. Now photographs have become an important part of our lives and have significant social role as they provide an affective communication link between friends and families. This research will be looking at how people relate themselves to pictures and if we could capture their emotions and expressions to store them in a way that is representative of their feelings towards the picture. As emotions can be associated with pictures of family, friends, places, holidays, social gatherings or travel to mention some of the aspects, we will also be analysing as to how emotions change over a period of time and if it could be represented accordingly in association with digital photos. This will allow us to look into the factors, which can make digital photo sharing a more fun and enjoyable experience. This research will involve user based evaluations and a scenario based approach for modelling the photo sharing interface. The creation of photo sharing system, based on this approach, is then investigated using the method of prototyping. The research shows methods, architectures, and tools used to make the development process more efficient and help assess the viability of the system under conditions that simulate to everyday use of the photo sharing software. To generalise and communicate results, the project will seek to simulate the home environment in a laboratory setting, using prototypes based on current high-end computing devices. The .Net technologies used for rapid prototyping is introduced. The aim of this research is to provide a demonstration environment of a photo sharing software for the smart home, which will allow for Reception/Viewing/distribution of photos within the home environment. The data could incorporate Audio/Video/Text/Photos, and user input etc. The research will also help us understand various aspects of how people interact with Digital Photos, what they would like to do, how can storing photos be made simpler, annotation of pictures, how can sharing photo’s made simpler, and how we can design an application which would allow users to attach emotions to pictures. The photo sharing system will provide an effective means to receive and distribute emotional information based on personal and temporal relationships associated with photos. The research also analyses the role of human computer interaction when developing ubiquitous technologies for the smart homes where information is embedded in the environment people live in. The research will present an overview of how photo sharing systems can be developed and evaluated using prototypes and user evaluations.

A “smarter grid” is expected to be more flexible and more reliable than traditional electric power grids. Among technologies required for the “smarter grid” deployment, this dissertation presents a sustainable microgrid and a spatial and temporal model of plug-in electric vehicle charging demand for the “smarter grid”. First, this dissertation proposes the dynamic modeling technique and operational strategies for a sustainable microgrid primarily powered by wind and solar energy resources. Multiple-input dc-dc converters are used to interface the renewable energy sources to the main dc bus. The intended application for such a microgrid is an area in which there is interest in achieving a sustainable energy solution, such as a telecommunication site or a residential area. Wind energy variations and rapidly changing solar irradiance are considered in order to explore the effect of such environmental variations to the intended microgrid. The proposed microgrid can be operated in an islanded mode in which it can continue to generate power during natural disasters or grid outages, thus improving disaster resiliency of the “smarter grid”. In addition, this dissertation presents the spatial and temporal model of electric vehicle charging demand for a rapid charging station located near a highway exit. Most previous studies have assumed a fixed charging location and fixed charging time during the off-peak hours for anticipating electric vehicle charging demand. Some other studies have based on limited charging scenarios at typical locations instead of a mathematical model. Therefore, from a distribution system perspective, electric vehicle charging demand is still unidentified quantity which may vary by space and time. In this context, this study proposes a mathematical model of electric vehicle charging demand for a rapid charging station. The mathematical model is based on the fluid dynamic traffic model and the M/M/s queueing theory. Firstly, the arrival rate of discharged vehicles at a charging station is predicted by the fluid dynamic model. Then, charging demand is forecasted by the M/M/s queueing theory with the arrival rate of discharged vehicles. The first letter M of M/M/s indicates that discharged vehicles arrive at a charging station with the Poisson distribution. The second letter M denotes that the time to charge each EV is exponentially distributed, and the third letter s means that there are s identical charging pumps at a charging station. This mathematical model of charging demand may allow grid’s distribution planners to anticipate charging demand at a specific charging station. / text

Since the inception of the Masdar Initiative in 2006, the Emirate of Abu Dhabi has invested a considerable amount of resources to promote renewable sources of energy like solar and wind. With an aim of achieving 7% of its electricity from renewable sources by the year 2020, there is much that the emirate needs to do in order to reduce its reliance on hydrocarbons while still planning capacity for future electricity demand. This report explores the effectiveness of a smart grid infrastructure as a mechanism to afford the flexibility and functionality required to incorporate renewable energy sources into the electric grid, as well as leveraging a real-time data network to attain reductions in peak demand consumption. Specific regulatory structures that exist in Abu Dhabi's electric and telecommunications markets are evaluated to understand the role they will play in dealing with interoperability standards, privacy concerns, and consumer participation issues that influence the effective integration of smart grid into Abu Dhabi's energy future. / text

Smart growth tools address a diverse range of specific concerns, including historic preservation, farmland protection, habitat conservation, flexible architectural design, and expedited land development. Smart growth unites the traditionally separate and competing growth promotion and growth control measures into a single growth accommodation approach. In addition to these important concepts, Henry R. Richmond posits that smart growth must now be explained within the context of “new urbanism” and “new regionalism.” What smart growth tries to accomplish is thus development with implied improvements in quality of life and environmental protection rather than mere urban growth or economic expansion per se. An important obstacle to smart growth measures is that growth problems rarely respect political boundaries. Scattered development patterns, as well as the traffic congestion, environmental degradation, fiscal stresses, and other problems that often accompany them, tend to be regional in nature, extending beyond the boundaries of any one locality. Accordingly, many growth problems are better addressed through regional solutions that federal, state and local smart growth measures my not provide. The general premise of “new regionalism” is that the economic health of the city and its outlying areas are inseparably intertwined, and that without regional planning and programs, individual jurisdictions in a single region compete with one another for limited resources and economic investment. New regionalists typically advocate from one of these three competing positions: greater economic prosperity, increased environmental protection, or improved social equity. Consequently, many politicians, advocates and activists are calling for the implementation of integrated policies that address the interrelatedness of all regional challenges, including housing, transportation, water, sewage, and other regional physical infrastructure systems. Denver evidences a suite of tensions between the promise and outcomes of planning with a wider, regional applicability. On the one hand, there is a progressiveness that embraces regional governance, growth management, economic vitality and quality infrastructure. But on the other hand, there is the reality of city sprawl, competitive local government relationships, and a convergence of interest between citizen choice and development industry behavior. This report will illustrate three issues regarding effective and efficient regional planning implementation at local, state and federal levels in the context of regional planning efforts in the Denver Metro Area. First, why does infill development and economic revitalization not only benefit the central city but the region as a whole? Secondly, how do land-use assignments and development design, like Smart Growth and New Urbanism, encourage regional planning efforts towards integrated mass transit? Finally, How does government fragmentation and overlap contribute to the lack of regional consensus and efficient planning? / text

Future smart grid systems will intelligently monitor and control energy flows in order to improve the efficiency and reliability of power delivery. This monitoring and control requires low-power, low-cost and highly reliable two-way communications between customers and utilities. To enable these two-way communication links, powerline communication (PLC) systems are attractive because they can be deployed over existing outdoor and indoor power lines. Power lines, however, have traditionally been designed for one-directional power delivery and remain hostile environments for communication signal propagation. In particular, non-Gaussian noise that is dominated by asynchronous impulsive noise and periodic impulsive noise, is one of the primary factors that limit the communication performance of PLC systems. For my PhD dissertation, I propose transmitter and receiver methods to mitigate the impact of asynchronous impulsive noise and periodic impulsive noise, respectively, on PLC systems. The methods exploit sparsity and/or cyclostationarity of the noise in both time and frequency domains, and require no or minor training overhead prior to data transmission. Compared to conventional PLC systems, the proposed transceivers achieve dramatic improvement (up to 1000x) in coded bit error rates in simulations, while maintaining similar throughput. / text

Residential buildings in the United States are responsible for the consumption of 38% of electricity, and for much of the fluctuations in the power demands on the electric grid, particularly in hot climates. Residential buildings are also where occupants spend nearly 69% of their time. As “smart” technologies, including electric grid-connected devices and home energy management systems are increasingly available and installed in buildings, this research focuses on the use of these technologies combined with available energy use data in accomplishing three main objectives. The research aims to: (a) better understand how residential buildings currently use electricity, (b) evaluate the use of these smart technologies and data to reduce buildings’ electricity use and their contribution to peak loads, and (c) develop a methodology to assess the impacts of these operational changes on occupant thermal comfort. Specifically this study focuses on two of the most significant electricity consumers in residential buildings: large appliances, including refrigerators, clothes washers, clothes dryers and dishwashers, and heating, ventilation and air conditioning (HVAC) systems. First, to develop an improved understanding of current electricity use patterns of large appliances and residential HVAC systems, this research analyzes a large set of field-collected data. This dataset includes highly granular electricity consumption information for residential buildings located in a hot and humid climate. The results show that refrigerators have the most reliable and consistent use, while the three user-dependent appliances varied more greatly among houses and by time-of-day. In addition, the daily use patterns of appliances vary in shape depending on a number of factors, particularly whether or not the occupants work from home, which contrasts with common residential building energy modeling assumptions. For the all-air central HVAC systems studied, the average annual HVAC duty cycle was found to be approximately 20%, and varied significantly depending on the season, time of day, and type of residential building. Duty cycle was also correlated to monthly energy use. This information provides an improvement to previously assumed values in indoor air modeling studies. Overall, the work presented here enhances the knowledge of how the largest consumers of residential buildings, large appliances and HVAC, operate and use energy, and identifies influential factors that affect these use patterns. The methodologies developed can be applied to determine use patterns for other energy consuming devices and types of buildings, to further expand the body of knowledge in this area. Expanding on this knowledge of current energy use, smart large appliances and residential HVAC systems are investigated for use in reducing peak electric grid loads, and building energy use, respectively. This includes a combination of laboratory testing, field-collected data, and modeling. For appliance peak load reduction, refrigerators are found to have a good demand response potential, in part due to the nearly 100% of residential buildings that have one or more of these appliances, and the predictability of their energy consumption behavior. Dryers provide less consistent energy use across all homes, but have a higher individual peak power demand during afternoon and evening peak use times. These characteristics also make dryers also a good candidate for demand response. The study of continuous commissioning of HVAC systems using energy data found that both runtime and energy use are increased, and cooling capacity and efficiency are reduced due to the presence of faults or inefficiencies. The correction of these faults have an estimated 1.4% to 5.7% annual impact on a residential building’s electricity use in a cooling-dominated climate such as the one studied. Overall, appliance peak load reduction results are useful for utility companies and policy makers in identifying what smart appliance may provide the most peak energy reduction potential through demand response programs. The results of the HVAC study provides a methodology that can be used with energy use data, to determine if an HVAC system has the characteristics implying an inefficiency may be present, and to quantify the annual savings resulting from its correction. The final aspect of this research focuses on the development of a tool to enable an assessment the effect of operational changes of a building associated with energy and peak load reduction on occupant comfort. This is accomplished by developing a methodology that uses the response surface methodology (RSM), combined with building performance data as input, and uncertainly analysis. A second-order RSM model constructed using a full-factorial design was generally found to provide strong agreement to in and out-of-sample building simulation data when evaluating the Average Percent of People Dissatisfied (PPD[subscript avg]). This 5-step methodology was applied to assess occupant thermal comfort in a residential building due to a 1-hour demand response event and a time-of-use pricing rate schedule for a variety of residential building characteristics. This methodology provides a model that can quickly assess, over a continuous range of values for each of the studied design variables, the effect on occupant comfort. This may be useful for building designers and operators who wish to quickly assess the effect of a change in building operations on occupants. / text

Machine learning methods are increasingly being used to solve real-world problems in the society. Often, the complexity of the methods are well hidden for users. However, integrating machine learning methods in real-world applications is not a straightforward process and requires knowledge both about the methods and domain knowledge of the problem. Two such domains are colour print quality assessment and anomaly detection in smart homes, which are currently driven by manual monitoring of complex situations. The goal of the presented work is to develop methods, algorithms and tools to facilitate monitoring and understanding of the complex situations which arise in colour print quality assessment and anomaly detection for smart homes. The proposed approach builds on the use and adaption of supervised and unsupervised machine learning methods. Novel algorithms for computing objective measures of print quality in production are proposed in this work. Objective measures are also modelled to study how paper and press parameters influence print quality. Moreover, a study on how print quality is perceived by humans is presented and experiments aiming to understand how subjective assessments of print quality relate to objective measurements are explained. The obtained results show that the objective measures reflect important aspects of print quality, these measures are also modelled with reasonable accuracy using paper and press parameters. The models of objective  measures are shown to reveal relationships consistent to known print quality phenomena. In the second part of this thesis the application area of anomaly detection in smart homes is explored. A method for modelling human behaviour patterns is proposed. The model is used in order to detect deviating behaviour patterns using contextual information from both time and space. The proposed behaviour pattern model is tested using simulated data and is shown to be suitable given four types of scenarios. The thesis shows that parts of offset lithographic printing, which traditionally is a human-centered process, can be automated by the introduction of image processing and machine learning methods. Moreover, it is concluded that in order to facilitate robust and accurate anomaly detection in smart homes, a holistic approach which makes use of several contextual aspects is required. / PPQ / SA3L