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- Genre: Doctoral Dissertation
Description
Distributed estimation uses many inexpensive sensors to compose an accurate estimate of a given parameter. It is frequently implemented using wireless sensor networks. There have been several studies on optimizing power allocation in wireless sensor networks used for distributed estimation, the vast majority of which assume linear radio-frequency amplifiers. Linear amplifiers are inherently inefficient, so in this dissertation nonlinear amplifiers are examined to gain efficiency while operating distributed sensor networks. This research presents a method to boost efficiency by operating the amplifiers in the nonlinear region of operation. Operating amplifiers nonlinearly presents new challenges. First, nonlinear amplifier characteristics change across manufacturing process variation, temperature, operating voltage, and aging. Secondly, the equations conventionally used for estimators and performance expectations in linear amplify-and-forward systems fail. To compensate for the first challenge, predistortion is utilized not to linearize amplifiers but rather to force them to fit a common nonlinear limiting amplifier model close to the inherent amplifier performance. This minimizes the power impact and the training requirements for predistortion. Second, new estimators are required that account for transmitter nonlinearity. This research derives analytically and confirms via simulation new estimators and performance expectation equations for use in nonlinear distributed estimation. An additional complication when operating nonlinear amplifiers in a wireless environment is the influence of varied and potentially unknown channel gains. The impact of these varied gains and both measurement and channel noise sources on estimation performance are analyzed in this paper. Techniques for minimizing the estimate variance are developed. It is shown that optimizing transmitter power allocation to minimize estimate variance for the most-compressed parameter measurement is equivalent to the problem for linear sensors. Finally, a method for operating distributed estimation in a multipath environment is presented that is capable of developing robust estimates for a wide range of Rician K-factors. This dissertation demonstrates that implementing distributed estimation using nonlinear sensors can boost system efficiency and is compatible with existing techniques from the literature for boosting efficiency at the system level via sensor power allocation. Nonlinear transmitters work best when channel gains are known and channel noise and receiver noise levels are low.
ContributorsSantucci, Robert (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioðlu, Cihan (Committee member) / Bakkaloglu, Bertan (Committee member) / Tsakalis, Kostas (Committee member) / Arizona State University (Publisher)
Created2013
Description
Energy consumption of the data centers worldwide is rapidly growing fueled by ever-increasing demand for Cloud computing applications ranging from social networking to e-commerce. Understandably, ensuring energy-efficiency and sustainability of Cloud data centers without compromising performance is important for both economic and environmental reasons. This dissertation develops a cyber-physical multi-tier server and workload management architecture which operates at the local and the global (geo-distributed) data center level. We devise optimization frameworks for each tier to optimize energy consumption, energy cost and carbon footprint of the data centers. The proposed solutions are aware of various energy management tradeoffs that manifest due to the cyber-physical interactions in data centers, while providing provable guarantee on the solutions' computation efficiency and energy/cost efficiency. The local data center level energy management takes into account the impact of server consolidation on the cooling energy, avoids cooling-computing power tradeoff, and optimizes the total energy (computing and cooling energy) considering the data centers' technology trends (servers' power proportionality and cooling system power efficiency). The global data center level cost management explores the diversity of the data centers to minimize the utility cost while satisfying the carbon cap requirement of the Cloud and while dealing with the adversity of the prediction error on the data center parameters. Finally, the synergy of the local and the global data center energy and cost optimization is shown to help towards achieving carbon neutrality (net-zero) in a cost efficient manner.
ContributorsAbbasi, Zahra (Author) / Gupta, Sandeep K. S. (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Shrivastava, Aviral (Committee member) / Wu, Carole-Jean (Committee member) / Arizona State University (Publisher)
Created2014
Description
Energy use within urban building stocks is continuing to increase globally as populations expand and access to electricity improves. This projected increase in demand could require deployment of new generation capacity, but there is potential to offset some of this demand through modification of the buildings themselves. Building stocks are quasi-permanent infrastructures which have enduring influence on urban energy consumption, and research is needed to understand: 1) how development patterns constrain energy use decisions and 2) how cities can achieve energy and environmental goals given the constraints of the stock. This requires a thorough evaluation of both the growth of the stock and as well as the spatial distribution of use throughout the city. In this dissertation, a case study in Los Angeles County, California (LAC) is used to quantify urban growth, forecast future energy use under climate change, and to make recommendations for mitigating energy consumption increases. A reproducible methodological framework is included for application to other urban areas.
In LAC, residential electricity demand could increase as much as 55-68% between 2020 and 2060, and building technology lock-in has constricted the options for mitigating energy demand, as major changes to the building stock itself are not possible, as only a small portion of the stock is turned over every year. Aggressive and timely efficiency upgrades to residential appliances and building thermal shells can significantly offset the projected increases, potentially avoiding installation of new generation capacity, but regulations on new construction will likely be ineffectual due to the long residence time of the stock (60+ years and increasing). These findings can be extrapolated to other U.S. cities where the majority of urban expansion has already occurred, such as the older cities on the eastern coast. U.S. population is projected to increase 40% by 2060, with growth occurring in the warmer southern and western regions. In these growing cities, improving new construction buildings can help offset electricity demand increases before the city reaches the lock-in phase.
In LAC, residential electricity demand could increase as much as 55-68% between 2020 and 2060, and building technology lock-in has constricted the options for mitigating energy demand, as major changes to the building stock itself are not possible, as only a small portion of the stock is turned over every year. Aggressive and timely efficiency upgrades to residential appliances and building thermal shells can significantly offset the projected increases, potentially avoiding installation of new generation capacity, but regulations on new construction will likely be ineffectual due to the long residence time of the stock (60+ years and increasing). These findings can be extrapolated to other U.S. cities where the majority of urban expansion has already occurred, such as the older cities on the eastern coast. U.S. population is projected to increase 40% by 2060, with growth occurring in the warmer southern and western regions. In these growing cities, improving new construction buildings can help offset electricity demand increases before the city reaches the lock-in phase.
ContributorsReyna, Janet Lorel (Author) / Chester, Mikhail V (Thesis advisor) / Gurney, Kevin (Committee member) / Reddy, T. Agami (Committee member) / Rey, Sergio (Committee member) / Arizona State University (Publisher)
Created2016
Description
Residential air conditioning systems represent a critical load for many electric
utilities, especially for those who serve customers in hot climates. In hot and dry
climates, in particular, the cooling load is usually relatively low during night hours and
early mornings and hits its maximum in the late afternoon. If electric loads could be
shifted from peak hours (e.g., late afternoon) to off-peak hours (e.g., late morning), not
only would building operation costs decrease, the need to run peaker plants, which
typically use more fossil fuels than non-peaker plants, would also decrease. Thus, shifting
electricity consumption from peak to off-peak hours promotes economic and
environmental savings. Operational and technological strategies can reduce the load
during peak hours by shifting cooling operation from on-peak hours to off-peak hours.
Although operational peak load shifting strategies such as precooling may require
mechanical cooling (e.g., in climates like Phoenix, Arizona), this cooling is less
expensive than on-peak cooling due to demand charges or time-based price plans.
Precooling is an operational shift, rather than a technological one, and is thus widely
accessible to utilities’ customer base. This dissertation compares the effects of different
precooling strategies in a Phoenix-based utility’s residential customer market and
assesses the impact of technological enhancements (e.g., energy efficiency measures and
solar photovoltaic system) on the performance of precooling. This dissertation focuses on
the operational and technological peak load shifting strategies that are feasible for
residential buildings and discusses the advantages of each in terms of peak energy
savings and residential electricity cost savings.
utilities, especially for those who serve customers in hot climates. In hot and dry
climates, in particular, the cooling load is usually relatively low during night hours and
early mornings and hits its maximum in the late afternoon. If electric loads could be
shifted from peak hours (e.g., late afternoon) to off-peak hours (e.g., late morning), not
only would building operation costs decrease, the need to run peaker plants, which
typically use more fossil fuels than non-peaker plants, would also decrease. Thus, shifting
electricity consumption from peak to off-peak hours promotes economic and
environmental savings. Operational and technological strategies can reduce the load
during peak hours by shifting cooling operation from on-peak hours to off-peak hours.
Although operational peak load shifting strategies such as precooling may require
mechanical cooling (e.g., in climates like Phoenix, Arizona), this cooling is less
expensive than on-peak cooling due to demand charges or time-based price plans.
Precooling is an operational shift, rather than a technological one, and is thus widely
accessible to utilities’ customer base. This dissertation compares the effects of different
precooling strategies in a Phoenix-based utility’s residential customer market and
assesses the impact of technological enhancements (e.g., energy efficiency measures and
solar photovoltaic system) on the performance of precooling. This dissertation focuses on
the operational and technological peak load shifting strategies that are feasible for
residential buildings and discusses the advantages of each in terms of peak energy
savings and residential electricity cost savings.
ContributorsArababadi, Reza (Author) / Parrish, Kristen (Thesis advisor) / Reddy, T A (Committee member) / Jackson, Roderick K (Committee member) / Arizona State University (Publisher)
Created2016
Description
User satisfaction is pivotal to the success of mobile applications. At the same time, it is imperative to maximize the energy efficiency of the mobile device to ensure optimal usage of the limited energy source available to mobile devices while maintaining the necessary levels of user satisfaction. However, this is complicated due to user interactions, numerous shared resources, and network conditions that produce substantial uncertainty to the mobile device's performance and power characteristics. In this dissertation, a new approach is presented to characterize and control mobile devices that accurately models these uncertainties. The proposed modeling framework is a completely data-driven approach to predicting power and performance. The approach makes no assumptions on the distributions of the underlying sources of uncertainty and is capable of predicting power and performance with over 93% accuracy.
Using this data-driven prediction framework, a closed-loop solution to the DEM problem is derived to maximize the energy efficiency of the mobile device subject to various thermal, reliability and deadline constraints. The design of the controller imposes minimal operational overhead and is able to tune the performance and power prediction models to changing system conditions. The proposed controller is implemented on a real mobile platform, the Google Pixel smartphone, and demonstrates a 19% improvement in energy efficiency over the standard frequency governor implemented on all Android devices.
Using this data-driven prediction framework, a closed-loop solution to the DEM problem is derived to maximize the energy efficiency of the mobile device subject to various thermal, reliability and deadline constraints. The design of the controller imposes minimal operational overhead and is able to tune the performance and power prediction models to changing system conditions. The proposed controller is implemented on a real mobile platform, the Google Pixel smartphone, and demonstrates a 19% improvement in energy efficiency over the standard frequency governor implemented on all Android devices.
ContributorsGaudette, Benjamin David (Author) / Vrudhula, Sarma (Thesis advisor) / Wu, Carole-Jean (Thesis advisor) / Fainekos, Georgios (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2017
Description
Failures in the cold chain, the system of refrigerated storage and transport that provides fresh produce or other essentials to be maintained at desired temperatures and environmental conditions, lead to food and energy waste. The mini container (MC) concept is introduced as an alternative to conventional refrigerated trucks (“reefers”), particularly for small growers. The energy consumption and corresponding GHG emissions for transporting tomatoes in two cities representing contrasting climates is analyzed for conventional reefers and the proposed mini containers. The results show that, for partial reefer loads, using the MCs reduces energy consumption and GHG emissions. The transient behavior of the vapor compression refrigeration cycle is analyzed by considering each component as a “lumped” system, and the resulting sub-models are solved using the Runge Kutta 4th-order method in a MATLAB code at hot and cold ambient temperatures. The time needed to reach steady state temperatures and the temperature values are determined. The maximum required compressor work in the transient phase and at steady state are computed, and as expected, as the ambient temperature increases, both values increase. Finally, the average coefficient of performance (COP) is determined for varying heat transfer coefficient values for the condenser and for the evaporator. The results show that the average COP increases as heat transfer coefficient values for the condenser and the evaporator increase. Starting the system from rest has an adverse effect on the COP due to the higher compressor load needed to change the temperature of the condenser and the evaporator. Finally, the impact on COP is analyzed by redirecting a fraction of the cold exhaust air to provide supplemental cooling of the condenser. It is noted that cooling the condenser improves the system's performance better than cooling the fresh air at 0% of returned air to the system.To sum up, the dissertation shows that the comparison between the conventional reefer and the MC illustrates the promising advantages of the MC, then a transient analysis is developed for deeply understanding the behaviors of the system component parameters, which leads finally to improvements in the system to enhance its performance.
ContributorsSyam, Mahmmoud Muhammed (Author) / Phelan, Patrick (Thesis advisor) / Villalobos, Rene (Thesis advisor) / Huang, Huei-Ping (Committee member) / Bocanegra, Luis (Committee member) / Al Omari, Salah (Committee member) / Arizona State University (Publisher)
Created2023