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Description
The overall energy consumption around the United States has not been reduced even with the advancement of technology over the past decades. Deficiencies exist between design and actual energy performances. Energy Infrastructure Systems (EIS) are impacted when the amount of energy production cannot be accurately and efficiently forecasted. Inaccurate engineering assumptions can result when there is a lack of understanding on how energy systems can operate in real-world applications. Energy systems are complex, which results in unknown system behaviors, due to an unknown structural system model. Currently, there exists a lack of data mining techniques in reverse engineering, which are needed to develop efficient structural system models. In this project, a new type of reverse engineering algorithm has been applied to a year's worth of energy data collected from an ASU research building called MacroTechnology Works, to identify the structural system model. Developing and understanding structural system models is the first step in creating accurate predictive analytics for energy production. The associative network of the building's data will be highlighted to accurately depict the structural model. This structural model will enhance energy infrastructure systems' energy efficiency, reduce energy waste, and narrow the gaps between energy infrastructure design, planning, operation and management (DPOM).
ContributorsCamarena, Raquel Jimenez (Author) / Chong, Oswald (Thesis director) / Ye, Nong (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Building construction, design and maintenance is a sector of engineering where improved efficiency will have immense impacts on resource consumption and environmental health. This research closely examines the Leadership in Environment and Energy Design (LEED) rating system and the International Green Construction Code (IgCC). The IgCC is a model code, written with the same structure as many building codes. It is a standard that can be enforced if a city's government decides to adopt it. When IgCC is enforced, the buildings either meet all of the requirements set forth in the document or it fails to meet the code standards. The LEED Rating System, on the other hand, is not a building code. LEED certified buildings are built according to the standards of their local jurisdiction and in addition to that, building owners can chose to pursue a LEED certification. This is a rating system that awards points based on the sustainable measures achieved by a building. A comparison of these green building systems highlights their accomplishments in terms of reduced electricity usage, usage of low-impact materials, indoor environmental quality and other innovative features. It was determined that in general IgCC is more holistic, stringent approach to green building. At the same time the LEED rating system a wider variety of green building options. In addition, building data from LEED certified buildings was complied and analyzed to understand important trends. Both of these methods are progressing towards low-impact, efficient infrastructure and a side-by-side comparison, as done in this research, shed light on the strengths and weaknesses of each method, allowing for future improvements.
ContributorsCampbell, Kaleigh Ruth (Author) / Chong, Oswald (Thesis director) / Parrish, Kristen (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This paper describes the research done to quantify the relationship between external air temperature and energy consumption and internal air temperature and energy consumption. The study was conducted on a LEED Gold certified building, College Avenue Commons, located on Arizona State University's Tempe campus. It includes information on the background of previous studies in the area, some that agree with the research hypotheses and some that take a different path. Real-time data was collected hourly for energy consumption and external air temperature. Intermittent internal air temperature was collected by undergraduate researcher, Charles Banke. Regression analysis was used to prove two research hypotheses. The authors found no correlation between external air temperature and energy consumption, nor did they find a relationship between internal air temperature and energy consumption. This paper also includes recommendations for future work to improve the study.
ContributorsBanke, Charles Michael (Author) / Chong, Oswald (Thesis director) / Parrish, Kristen (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This is an applied research paper, where a micro campus was designed for the San Carlos Apache Community with a goal of meeting requirements for at least three petals of Living Building Challenge. The end goal was to submit recommendations for attaining the petal certifications. The process of design not only included following spatial requirements of designing the building, but also including a wider perspective of construction and energy management in it. The first step of the research was getting to know the community and their requirements and priorities. This was done in 1st semester as a part of an applied class Indigenous Project Delivery. The second part of the research was to design a micro campus for the community that is in sync with the main campus. The intent of design is to respect the community’s culture and help them pass it on to the next generation while abiding by the Living Building Challenge standards. The third step of this research was to back up the design with recommendations for petal certifications.
ContributorsSingaraju, Meghana (Author) / Costa, Wanda Dalla (Thesis advisor) / Coseo, Paul (Committee member) / Vekstein, Claudio (Committee member) / Parrish, Kristen (Committee member) / Arizona State University (Publisher)
Created2022
Description
This paper begins with an introduction to the topics relevant to the research presented. Properties of diamond, diamond’s ability to be used in power electronics compared to other semiconducting materials, and a brief overview of field effect transistors are among the topics discussed. The remainder of the paper centers around research that has been conducted on seven diamond samples. Interface characterization was performed on two diamond samples, one with a high boron incorporation epitaxial layer and another with a low boron incorporation epitaxial layer. UPS He I analysis and UPS He II analysis were used to construct band alignments for the two samples, which revealed no significant differences between their measured properties. A Python program designed to optimize XPS loss peak and UPS He II graphical data analysis is also discussed in detail. Next, Hall effect measurements are examined. Hall effect measurements were carried out on seven diamond samples, two of which have high boron incorporation epitaxial layers, two of which have low boron incorporation epitaxial layers, one of which has a moderate boron incorporation epitaxial layer, and two of which have a phosphorus-doped epitaxial layer. Hall measurements of the boron-doped samples revealed no significant differences in measured parameters amongst the samples with varying boron incorporation epitaxial layers, with the exception of an expected difference in measured carrier concentration proportional to the amount of dopant incorporation in the layers. Some samples with boron-doped epitaxial layers produced measurements indicating n-type charge carriers, which is unexpected given the p-type charge carriers within these samples. The phosphorus-doped samples were unable to be measured due to overly high resistance following an oxygen termination step, and this effect was functionally reversed following hydrogen termination of the samples. It is hypothesized that Fermi pinning is responsible for this effect. The paper concludes with a summary of data discussed in previous sections and a suggested direction for future research on this topic.
ContributorsJacobs, Madeleine (Author) / Nemanich, Robert (Thesis director) / Botana, Antia (Committee member) / Barrett, The Honors College (Contributor) / College of Integrative Sciences and Arts (Contributor)
Created2022-05
Description
One of the key infrastructures of any community or facility is the energy system which consists of utility power plants, distributed generation technologies, and building heating and cooling systems. In general, there are two dimensions to “sustainability” as it applies to an engineered system. It needs to be designed, operated, and managed such that its environmental impacts and costs are minimal (energy efficient design and operation), and also be designed and configured in a way that it is resilient in confronting disruptions posed by natural, manmade, or random events. In this regard, development of quantitative sustainability metrics in support of decision-making relevant to design, future growth planning, and day-to-day operation of such systems would be of great value. In this study, a pragmatic performance-based sustainability assessment framework and quantitative indices are developed towards this end whereby sustainability goals and concepts can be translated and integrated into engineering practices.
New quantitative sustainability indices are proposed to capture the energy system environmental impacts, economic performance, and resilience attributes, characterized by normalized environmental/health externalities, energy costs, and penalty costs respectively. A comprehensive Life Cycle Assessment is proposed which includes externalities due to emissions from different supply and demand-side energy systems specific to the regional power generation energy portfolio mix. An approach based on external costs, i.e. the monetized health and environmental impacts, was used to quantify adverse consequences associated with different energy system components.
Further, this thesis also proposes a new performance-based method for characterizing and assessing resilience of multi-functional demand-side engineered systems. Through modeling of system response to potential internal and external failures during different operational temporal periods reflective of diurnal variation in loads and services, the proposed methodology quantifies resilience of the system based on imposed penalty costs to the system stakeholders due to undelivered or interrupted services and/or non-optimal system performance.
A conceptual diagram called “Sustainability Compass” is also proposed which facilitates communicating the assessment results and allow better decision-analysis through illustration of different system attributes and trade-offs between different alternatives. The proposed methodologies have been illustrated using end-use monitored data for whole year operation of a university campus energy system.
New quantitative sustainability indices are proposed to capture the energy system environmental impacts, economic performance, and resilience attributes, characterized by normalized environmental/health externalities, energy costs, and penalty costs respectively. A comprehensive Life Cycle Assessment is proposed which includes externalities due to emissions from different supply and demand-side energy systems specific to the regional power generation energy portfolio mix. An approach based on external costs, i.e. the monetized health and environmental impacts, was used to quantify adverse consequences associated with different energy system components.
Further, this thesis also proposes a new performance-based method for characterizing and assessing resilience of multi-functional demand-side engineered systems. Through modeling of system response to potential internal and external failures during different operational temporal periods reflective of diurnal variation in loads and services, the proposed methodology quantifies resilience of the system based on imposed penalty costs to the system stakeholders due to undelivered or interrupted services and/or non-optimal system performance.
A conceptual diagram called “Sustainability Compass” is also proposed which facilitates communicating the assessment results and allow better decision-analysis through illustration of different system attributes and trade-offs between different alternatives. The proposed methodologies have been illustrated using end-use monitored data for whole year operation of a university campus energy system.
ContributorsMoslehi, Salim (Author) / Reddy, T. Agami (Thesis advisor) / Lackner, Klaus S (Committee member) / Parrish, Kristen (Committee member) / Pendyala, Ram M. (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2018
Description
The solar energy sector has been growing rapidly over the past decade. Growth in renewable electricity generation using photovoltaic (PV) systems is accompanied by an increased awareness of the fault conditions developing during the operational lifetime of these systems. While the annual energy losses caused by faults in PV systems could reach up to 18.9% of their total capacity, emerging technologies and models are driving for greater efficiency to assure the reliability of a product under its actual application. The objectives of this dissertation consist of (1) reviewing the state of the art and practice of prognostics and health management for the Direct Current (DC) side of photovoltaic systems; (2) assessing the corrosion of the driven posts supporting PV structures in utility scale plants; and (3) assessing the probabilistic risk associated with the failure of polymeric materials that are used in tracker and fixed tilt systems.
As photovoltaic systems age under relatively harsh and changing environmental conditions, several potential fault conditions can develop during the operational lifetime including corrosion of supporting structures and failures of polymeric materials. The ability to accurately predict the remaining useful life of photovoltaic systems is critical for plants ‘continuous operation. This research contributes to the body of knowledge of PV systems reliability by: (1) developing a meta-model of the expected service life of mounting structures; (2) creating decision frameworks and tools to support practitioners in mitigating risks; (3) and supporting material selection for fielded and future photovoltaic systems. The newly developed frameworks were validated by a global solar company.
As photovoltaic systems age under relatively harsh and changing environmental conditions, several potential fault conditions can develop during the operational lifetime including corrosion of supporting structures and failures of polymeric materials. The ability to accurately predict the remaining useful life of photovoltaic systems is critical for plants ‘continuous operation. This research contributes to the body of knowledge of PV systems reliability by: (1) developing a meta-model of the expected service life of mounting structures; (2) creating decision frameworks and tools to support practitioners in mitigating risks; (3) and supporting material selection for fielded and future photovoltaic systems. The newly developed frameworks were validated by a global solar company.
ContributorsChokor, Abbas (Author) / El Asmar, Mounir (Thesis advisor) / Chong, Oswald (Committee member) / Ernzen, James (Committee member) / Arizona State University (Publisher)
Created2017
Description
Commercial buildings in the United States account for 19% of the total energy consumption annually. Commercial Building Energy Consumption Survey (CBECS), which serves as the benchmark for all the commercial buildings provides critical input for EnergyStar models. Smart energy management technologies, sensors, innovative demand response programs, and updated versions of certification programs elevate the opportunity to mitigate energy-related problems (blackouts and overproduction) and guides energy managers to optimize the consumption characteristics. With increasing advancements in technologies relying on the ‘Big Data,' codes and certification programs such as the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), and the Leadership in Energy and Environmental Design (LEED) evaluates during the pre-construction phase. It is mostly carried out with the assumed quantitative and qualitative values calculated from energy models such as Energy Plus and E-quest. However, the energy consumption analysis through Knowledge Discovery in Databases (KDD) is not commonly used by energy managers to perform complete implementation, causing the need for better energy analytic framework.
The dissertation utilizes Interval Data (ID) and establishes three different frameworks to identify electricity losses, predict electricity consumption and detect anomalies using data mining, deep learning, and mathematical models. The process of energy analytics integrates with the computational science and contributes to several objectives which are to
1. Develop a framework to identify both technical and non-technical losses using clustering and semi-supervised learning techniques.
2. Develop an integrated framework to predict electricity consumption using wavelet based data transformation model and deep learning algorithms.
3. Develop a framework to detect anomalies using ensemble empirical mode decomposition and isolation forest algorithms.
With a thorough research background, the first phase details on performing data analytics on the demand-supply database to determine the potential energy loss reduction potentials. Data preprocessing and electricity prediction framework in the second phase integrates mathematical models and deep learning algorithms to accurately predict consumption. The third phase employs data decomposition model and data mining techniques to detect the anomalies of institutional buildings.
The dissertation utilizes Interval Data (ID) and establishes three different frameworks to identify electricity losses, predict electricity consumption and detect anomalies using data mining, deep learning, and mathematical models. The process of energy analytics integrates with the computational science and contributes to several objectives which are to
1. Develop a framework to identify both technical and non-technical losses using clustering and semi-supervised learning techniques.
2. Develop an integrated framework to predict electricity consumption using wavelet based data transformation model and deep learning algorithms.
3. Develop a framework to detect anomalies using ensemble empirical mode decomposition and isolation forest algorithms.
With a thorough research background, the first phase details on performing data analytics on the demand-supply database to determine the potential energy loss reduction potentials. Data preprocessing and electricity prediction framework in the second phase integrates mathematical models and deep learning algorithms to accurately predict consumption. The third phase employs data decomposition model and data mining techniques to detect the anomalies of institutional buildings.
ContributorsNaganathan, Hariharan (Author) / Chong, Oswald W (Thesis advisor) / Ariaratnam, Samuel T (Committee member) / Parrish, Kristen (Committee member) / Arizona State University (Publisher)
Created2017
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