<p>In this thesis we consider low complexity downlink traffic scheduling for green vehicular roadside infrastructure. In multiple roadside unit (RSU) deployments, the energy provisioning of the RSUs may differ, and it is therefore desirable to balance RSU usage from a normalized energy viewpoint. We consider both splittable and unsplittable RSU assignment scheduling (SRA and URA). We first derive an offline integer linear programming bound for the normalized min-max RSU energy usage, which can be solved for a given input sample function. We then show that in the SRA case there is a polynomial complexity 2-approximation bound for the normalized min-max energy schedule. These bounds are used for comparisons with several proposed online scheduling algorithms. The first scheduler is a low complexity Greedy Online Algorithm (GOA) that makes greedy RSU selections followed by minimum energy time slot assignments. A normalized min-max online algorithm is then proposed (TOAA) which is an online version of the 2-approximation bound for SRA scheduling. Then, the Greedy Flow Graph Algorithm (GFGA), which makes greedy RSU selections followed by time slots reassignment whenever a new vehicle is assigned to the same RSU. This is done using a locally optimum integer linear program that can be efficiently solved using a minimum cost flow graph. Two low complexity algorithms are then introduced based on a potential function scheduling approach. The One-Objective algorithm, uses a primary objective based on normalized min-max energy. The second, the Bi-Objective algorithm, uses the same primary objective combined with a total energy secondary objective. These algorithms have provable performance guarantees, in that their worse-case competitive ratio performance is upper bounded. Results from a variety of experiments show that the proposed scheduling algorithms perform well. In particular, we find that in the SRA case, the TOAA and GFGA algorithms perform very close to the lower bound, but at the expense of having to reassign time slots whenever a new vehicle arrives. In the URA case, our low complexity One-Objective algorithm performs better than the others over a wide range of traffic conditions.</p> / Master of Applied Science (MASc)

<p>Smart scheduling can be used to reduce infrastructure-to-vehicle</p> <p>energy costs in delay tolerant vehicular</p> <p>networks (Hammad et al., 2010).. In this thesis we show that by combining</p> <p>this with vehicle-to-vehicle (V2V) forwarding, energy efficiency can</p> <p>be increased beyond that possible in the single hop case. This is</p> <p>accomplished by having the roadside infrastructure forward packets</p> <p>through vehicles which are in energy favourable locations. We first</p> <p>derive offline bounds on the downlink energy usage for a given input</p> <p>sample function when V2V forwarding is used. Separate bounds are given</p> <p>for the off-channel and in-channel forwarding cases. These bounds are</p> <p>used for comparisons with a variety of proposed online scheduling</p> <p>algorithms. The paper then introduces online algorithms for both</p> <p>fixed bit rate and variable bit rate air interface options. The first</p> <p>algorithm is based on a greedy local optimization (GLOA). A version of</p> <p>this algorithm which uses a minimum cost flow graph scheduler is also</p> <p>introduced. A more sophisticated algorithm is then proposed which is</p> <p>based on a finite window group optimization (FWGO). Versions of these</p> <p>algorithms are also proposed which use in-channel vehicle-to-vehicle</p> <p>scheduling. The proposed algorithms are also adapted to the variable</p> <p>bit rate air interface case. Results from a variety of experiments</p> <p>show that the proposed scheduling algorithms can significantly improve</p> <p>the downlink energy requirements of the roadside unit compared to the</p> <p>case where vehicle-to-vehicle packet forwarding is not used. The</p> <p>performance improvements are especially strong under heavy loading</p> <p>conditions and when the variation in vehicle communication</p> <p>requirements or vehicle speed is high.</p> / Master of Applied Science (MASc)

New windows are rated based on their energy performance during the use phase. This rating neglects the overall environmental impact caused by raw material extraction, manufacturing, maintenance and disposal. Due to the number of residential window replacements occurring today in the United States, there is a growing need to quantify the sustainability of window preservation as an alternative to window replacement. This study assessed the environmental impact of historic wood window restoration versus window replacement for the entire "cradle to grave" life cycle of the window assembly. This study focused on a typical, mid-twentieth century housing development in the Northeast United States using four window configurations as follows: 1. Restored original wood window with a new exterior aluminum storm window; 2. PVC replacement window; 3. Aluminum-clad wood replacement window; 4. Wood replacement window. The dissertation assessed the life cycle of window configurations using GaBi Software. The life cycle inventories were analyzed using the TRACI 2.1 impact method which translated the environmental consequences of the life cycle assessment processes into quantifiable environmental impacts. The dissertation also considered window thermal performance and life cycle costs. When considering life cycle environmental impacts, thermal performance, energy savings and material costs, the results indicated that wood window restoration was the best option when compared to replacement windows considered in this study; however, the results indicated that building service life and window service life assumptions could impact results. Thermal performance testing of windows revealed that window restoration techniques undertaken in this study improved the window's overall thermal performance. The testing also indicated that the effects of air infiltration had minimal influence on the performance of the restored window assembly when compared to a high performance replacement window. The results of the energy model exhibited only a small annual energy savings between the restored window assembly and a high performance replacement window. The payback cost analysis revealed that, while there was an immediate financial benefit of window replacement with the PVC option, window replacement frequency and overall life cycle environmental impacts would favor the restored window option. / Civil Engineering

Yes / Technological systems are not merely designed with a narrow function in mind. Good designs typically aim at reducing operational costs, e.g. through achieving high energy efficiency and improved dependability (i.e. reliability, availability and maintainability). When there is a choice of alternative design options that perform the same function, it makes sense to compare alternatives so that the variant that minimises operational costs can be selected. In this paper, we examine this issue in the context of the design of Waste Heat Recovery Systems (WHRS) for main engines of large commercial freight vessels. We propose a method that can predict the operational cost of a WHRS via thermodynamic analysis which shows costs related to energy utilisation, and dependability analysis which shows costs related to system unavailability and repair. Our approach builds on recent advances in thermodynamic simulation and compositional dependability analysis techniques. It is a model-based approach, and allows reuse of component libraries, and a high degree of automation which simplify application of the method. Our case study shows that alternative designs can be explored in fast iterations of this method, and that this facilitates the evidence-based selection of a design that minimises operational costs.

Energy efficiency is a critical feature of wireless sensor networks (WSNs), because sensor nodes run on batteries that are generally difficult to recharge once deployed. For target tracking---one of the most important WSN application types---energy efficiency needs to be considered in various forms and shapes, such as idle listening, trajectory estimation, and data propagation. In this dissertation, we study three correlated problems on energy efficient target tracking in WSNs: sleep scheduling, particle filtering, and constrained flooding. We develop a Target Prediction and Sleep Scheduling protocol (TPSS) to improve energy efficiency for idle listening. We start with designing a target prediction method based on both kinematics and probability. Based on target prediction and proactive wake-up, TPSS precisely selects the nodes to awaken and reduces their active time, so as to enhance energy efficiency with limited tracking performance loss. In addition, we expand Sleep Scheduling to Multiple Target Tracking (SSMTT), and further reduce the energy consumption by leveraging the redundant alarm messages of interfering targets. Our simulation-based experimental studies show that compared to existing protocols such as Circle scheme and MCTA, TPSS and SSMTT introduce an improvement of 25% ~ 45% on energy efficiency, at the expense of only 5% ~ 15% increase on the detection delay. Particle Filtering is one of the most widely used Bayesian estimation methods, when target tracking is considered as a dynamic state estimation problem for trajectory estimation. However, the significant computational and communication complexity prohibits its application in WSNs. We design two particle filters (PFs)---Vector space based Particle Filter (VPF) and Completely Distributed Particle Filter (CDPF)---to improve energy efficiency of PFs by reducing the number of particles and the communication cost. Our experimental evaluations show that even though VPF incurs 34% more estimation error than RPF, and CDPF incurs a similar estimation error to SDPF, they significantly improve the energy efficiency by as much as 68% and 90% respectively. For data propagation, we present a Constrained Flooding protocol (CFlood) to enhance energy efficiency by increasing the deadline satisfaction ratio per unit energy consumption of time-sensitive packets. CFlood improves real-time performance by flooding, but effectively constrains energy consumption by controlling the scale of flooding---i.e., flooding only when necessary. If unicasting meets the distributed sub-deadline at a hop, CFlood aborts further flooding even after flooding has occurred in the current hop. Our simulation-based experimental studies show that CFlood achieves higher deadline satisfaction ratio per unit energy consumption by as much as 197%, 346%, and 20% than existing multipath forwarding protocols, namely, Mint Routing, MCMP and DFP respectively, especially in sparsely deployed or unreliable sensor network environments. To verify the performance and efficiency of the dissertation's solutions, we developed a prototype implementation based on TelosB motes and TinyOS version 2.1.1. In the field experiments, we compared TPSS, VPF, CDPF, and CFlood algorithms/protocols to their respective competing efforts. Our implementation measurements not only verified the rationality and feasibility of the proposed solutions for target tracking in WSNs, but also strengthened the observations on their efficiency from the simulation. / Ph. D.

Residential water heating is linked to the primary source of waterborne disease outbreaks in the United States, and accounts for greater energy demand than the combined water/wastewater utility sector. To date, there has been little research that can guide decision-making with regards to water heater selection and operation to minimize energy costs and the likelihood of waterborne disease. We have outlined three types of systems that currently dominate the marketplace: 1) a standard hot water tank with no hot water recirculation (STAND), 2) a hot water tank with hot water recirculation (RECIRC), and 3) an on-demand tankless hot water system with no hot water recirculation (DEMAND). Not only did the standard system outperform the hot water recirculation system with respect to temperature profile during flushing, but STAND also operated with 32 – 36% more energy efficiency. Although RECIRC did in fact save some water at the tap, when factoring in the energy efficiency reductions and associated water demand, RECIRC actually consumed up to 7 gpd more and cost consumers more money. DEMAND operated with virtually 100% energy efficiency, but cannot be used in many circumstances dependent on scaling and incoming water temperature, and may require expensive upgrades to home electrical systems. RECIRC had greater volumes at risk for pathogen growth when set at the lower end of accepted temperature ranges, and lower volumes at risk when set at the higher end when compared to STAND. RECIRC also tended to have much lower levels of disinfectant residual (40 -850%), 4-6 times as much hydrogen, and 3-20 times more sediment compared to standard tanks without recirculation. DEMAND had very small volumes of water at risk and relatively high levels of disinfection. A comparison study of optimized RECIRC conditions was compared to the baseline modes of operation. Optimization increased energy efficiency 5.5 – 60%, could save consumers 5 – 140% and increased the disinfectant residual up to 560% higher disinfectant residual as compared to the baseline RECIRC system. STAND systems were still between 3 – 55% more energy efficient and could save consumers between $19 - $158 annual on water and electrical costs. Thus, in the context of “green” design, RECIRC systems provide a convenience to consumers in the form of nearly instant hot water, at a cost of higher capital, operating and overall energy costs. / Ph. D.

The work presented in this thesis focuses on reducing the energy demand of a residential building by using a coupled ground-source heat pump/energy recovery ventilation (GSHP-ERV) system to present a novel approach to space condition and domestic hot water supply for a residence. The proposed system is capable of providing hot water on-demand with a high coefficient of performance (COP), thus eliminating the need for a hot water storage tank and circulation system while requiring little power consumption. The necessary size of the proposed system and the maximum and normal heating and cooling loads for the home were calculated based on the assumptions of an energy efficient home, the assumed construction specifications, and the climate characteristics of the Blacksburg, Virginia region. The results from the load analysis were used to predict energy consumption and costs associated with annual operations.The results for the predicted heating annual energy consumption and costs for the GSHP-ERV system were compared to an air-source heat pump and a natural gas furnace. On average, it was determined that the proposed system was capable of reducing annual energy consumption by 56-78% over air-source heat pumps and 85-88% over a natural gas furnace. The proposed GSHP-ERV system reduced costs by 45-61% over air-source heat pump systems and 52-58% over natural gas furnaces. The annual energy consumption and costs associated with cooling were not calculated as cooling accounts for a negligible portion (6%) of the total annual energy demand for a home in Blacksburg. / Master of Science

Seven to ten percent of the energy used globally goes towards powering information and communications technology (ICT): the global data- and telecommunications network, the private and commercial datacenters it supports, and the 19 billion electronic devices around the globe it interconnects, through which we communicate, and access and produce information. As bandwidth and data rates increase, so does the volume of traffic, as well as the absolute amount of new information digitized and uploaded onto the Net and into the cloud each second. Words like gigabit and terabyte were needless fifteen years ago in the public arena; now, they are common phrases. As people use their networked devices to do more, to access more, to send more, and to connect more, they use more energy--not only in their own devices, but also throughout the ICT. While there are many endeavors focused on individual low-power devices, few are examining broad strategies that cross the many boundaries of separate concerns within the ICT; also, few are assessing the impact of specific strategies on the global energy supply: at a global scale. This work examines the energy savings of several such strategies; it also assesses their efficacy in reducing energy consumption, both within specific networks and within the larger ICT. All of these strategies save energy by reducing the work done by the system as a whole on behalf of a single user, often by exploiting commonalities among what many users around the globe are also doing to amortize the costs. / Ph. D.

Cities occupy 2% of the earth's surface, and yet consume 75% of the world's resources. As a major contributor to rapidly growing global energy expenditures, urban buildings are often designed and operated inefficiently despite their significant contributions to carbon emissions, triggering environmental deterioration locally and worldwide. Moreover, ongoing industrialization and urbanization pose challenges for achieving a more sustained and resilient built environment. The goal of this PhD research is to advance our understanding of urban building networks' thermal-energy dynamics in order to achieve sustainable energy conservation in the built environment. Considering buildings as networks rather than as stand-alone entities highlights the inextricably linked and interwoven relationship between urban micro-climates and buildings. With this approach, I strive to explore, mitigate, and optimize the mutual influences of the Inter-Building Effect (IBE) in dense urban settings through numerical and empirical analyses. My research also draws inspiration for investigating solutions to complex engineering problems from nature, as I seek to understand synergies between building and biological systems to discover innovative connections and integrate biology to transform buildings through sustainable building network designs. This dissertation contains three interdependent projects to explore, mitigate and optimize the IBE, respectively. I first developed a systematic approach to separately assess the complex interactions that constitute the IBE in dense urban settings and conducted cross-regional analyses in a dynamic simulation environment. Having disaggregated, quantified and understood the effects of mutual shading and mutual reflection within a network of buildings, I then, in the second project, examined different measures to mitigate the negative IBE impact under certain circumstances (e.g. directional reflective optical properties of building facades and thermal storage technologies). These two projects extended prior work that examined the potential for a biological system retroreflective surface to reduce IBE in urban building networks. Therefore, in my third project, I introduced a broad framework that draws parallels between natural and built environment systems through a levels-of-organization perspective leading to the search for an optimal status of the IBE. Inspired from a self-regulating phenomenon of plant density, I presented and discussed an approach to determine optimal urban building network density as an example for how this framework can support cross-level assessment. The findings expand and deepen our understanding of the IBE and provide insights on the strategies to mitigate the negative mutual impact within dense urban building networks. This research contributes a unique and holistic perspective on the interdependencies in the urban building network system. To design density-optimal building networks will become increasingly important to sustainable urban development and smart growth as clusters of dense urban settings continue to grow due to rapid urbanization and population migration in the next few decades. / Ph. D.

In the United States, over 40% of the country's total energy consumption is in buildings, most of which are either small-sized (<5,000 sqft) or medium-sized (5,000-50,000 sqft). These buildings offer excellent opportunities for energy saving and demand response (DR), but these opportunities are rarely utilized due to lack of effective building energy management systems and automated algorithms that can assist a building to participate in a DR program. Considering the low load factor in US and many other countries, DR can serve as an effective tool to reduce peak demand through demand-side load curtailment. A convenient option for the customer to benefit from a DR program is to use automated DR algorithms within a software that can learn user comfort preferences for the building loads and make automated load curtailment decisions without affecting customer comfort. The objective of this dissertation is to provide such a solution. First, this dissertation contributes to the development of key features of a building energy management open source software platform that enable ease-of-use through plug and play and interoperability of devices in a building, cost-effectiveness through deployment in a low-cost computer, and DR through communication infrastructure between building and utility and among multiple buildings, while ensuring security of the platform. Second, a set of reinforcement learning (RL) based algorithms is proposed for the three main types of loads in a building: heating, ventilation and air conditioning (HVAC) loads, lighting loads and plug loads. In absence of a DR program, these distributed agent-based learning algorithms are designed to learn the user comfort ranges through explorative interaction with the environment and accumulating user feedback, and then operate through policies that favor maximum user benefit in terms of saving energy while ensuring comfort. Third, two sets of DR algorithms are proposed for an incentive-based DR program in a building. A user-defined priority based DR algorithm with smart thermostat control and utilization of distributed energy resources (DER) is proposed for residential buildings. For commercial buildings, a learning-based algorithm is proposed that utilizes the learning from the RL algorithms to use a pre-cooling/pre-heating based load reduction method for HVAC loads and a mixed integer linear programming (MILP) based optimization method for other loads to dynamically maintain total building demand below a demand limit set by the utility during a DR event, while minimizing total user discomfort. A user defined priority based DR algorithm is also proposed for multiple buildings in a community so that they can participate in realizing combined DR objectives. The software solution proposed in this dissertation is expected to encourage increased participation of smaller and medium-sized buildings in demand response and energy saving activities. This will help in alleviating power system stress conditions by employing the untapped DR potential in such buildings. / Ph. D.