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Department of Systems Engineering

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https://hdl.handle.net/10217/199888
This digital collection includes faculty/student publications, theses, dissertations, and datasets from the Department of Systems Engineering.

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Browsing Department of Systems Engineering by Author "Aloise-Young, Patricia, committee member"

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    Characterizing and improving the adoption rate of model-based systems engineering through an application of the Diffusion of Innovations theory
    (Colorado State University. Libraries, 2024) Call, Daniel R., author; Herber, Daniel R., advisor; Aloise-Young, Patricia, committee member; Conrad, Steven, committee member; Shahroudi, Kamran Eftekhari, committee member
    As the environment and operational context of new systems continue to evolve and become increasingly complex, the practice of systems engineering (SE) must adapt accordingly. A great deal of research and development has gone and continues to go into formulating and maturing a model-based approach to SE that addresses many of the shortcomings of a conventional, document-based SE approach. In spite of the work that has been done to advance the practice of model-based systems engineering (MBSE), it has not yet been adopted to a level that would be expected based on its demonstrated benefits. While research continues into even more effective MBSE approaches, there is a need to ascertain why extant MBSE innovations are not being adopted more widely, and if possible, determine a way to accelerate its adoption. This outcome is particularly important as MBSE is a key enabler to an agile systems engineering (ASE) approach that satisfies the desire of many stakeholders to apply agile principles to SE processes. The diffusion of innovations (DoI) theory provides a useful framework for understanding the factors that affect the adoption rate of innovations in many fields. This theory has not only been effective at explaining why innovations are adopted but has also been able to explain why objectively superior innovations are not adopted. The DoI theory is likely to provide insight into the factors that are depressing the adoption rate of MBSE. Despite prior efforts in the SE community to promote MBSE, the DoI theory has not been directly and deliberately applied to understand what is preventing widespread MBSE adoption. Some elements of the theory appear in the literature addressing MBSE adoption challenges without any recognition of awareness of the theory and its implications. The expectation is that harnessing the insights offered by this theory will lead to MBSE presentation and implementation strategies that will increase its use. This would allow its benefits to be more widely realized in the SE community and improve the practice of SE generally to address modern, complex environments. The DoI theory has shown that the most significant driver of adoption rate variability is the perceived attributes of the innovation in question. A survey is a useful tool to discover the perceptions of potential adopters of an innovation. The primary contribution of this research is the development of a survey to capture and assess a participant's perceptions of specified attributes of MBSE, their current use of MBSE, and some limited demographic information. This survey was widely distributed to gather data on current perceptions of MBSE in the SE community. Survey results highlighted that respondents recognize the relative advantage of MBSE in improving data quality and traceability, but perceived complexity and compatibility with existing practices still present barriers to adoption. Subpopulation analysis reveals that those who are not already involved in MBSE efforts face the additional adoption obstacles of limited trial opportunities and tool access (chi-squared test of independence between these populations resulted in p = 0.00). The survey underscores the potential for closer alignment between MBSE and existing SE methodologies to improve the perceived compatibility of MBSE. Targeted actions are proposed to address these barriers to adoption. These targeted actions include improving the availability and use of reusable model elements to expedite system model development, improved tailoring of MBSE approaches to better suit organizational needs, an increased emphasis on ASE, refining MBSE approaches to reduce the perceived mental effort required, a lowering of the barrier to entry for MBSE by improving access to the resources (tool, time, and training) required to experiment with MBSE, and increased efforts to identify and execute relevant MBSE pilot projects. The lessons and principles from the DoI theory should be applied to take advantage of the opportunity afforded by the release of SysML v2 to reframe perceptions of MBSE. Future studies would benefit from examining additional variables identified by the DoI theory, incorporating control questions to differentiate between perceptions of SE generally and MBSE specifically, identifying better methods to assess current MBSE use by participants, and measures to broaden the participant scope.
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    Disaggregation of net-metered advanced metering infrastructure data to estimate photovoltaic generation
    (Colorado State University. Libraries, 2019) Stainsby, Wendell Jay, author; Young, Peter, advisor; Zimmerle, Daniel, committee member; Aloise-Young, Patricia, committee member
    Advanced metering infrastructure (AMI) is a system of smart meters and data management systems that enables communication between a utility and a customer's premise, and can provide real time information about a solar array's production. Due to residential solar systems typically being configured behind-the-meter, utilities often have very little information about their energy generation. In these instances, net-metered AMI data does not provide clear insight into PV system performance. This work presents a methodology for modeling individual array and system-wide PV generation using only weather data, premise AMI data, and the approximate date of PV installation. Nearly 850 homes with installed solar in Fort Collins, Colorado, USA were modeled for up to 36 months. By matching comparable periods of time to factor out sources of variability in a building's electrical load, algorithms are used to estimate the building's consumption, allowing the previously invisible solar generation to be calculated. These modeled outputs are then compared to previously developed white-box physical models. Using this new AMI method, individual premises can be modeled to agreement with physical models within ±20%. When modeling portfolio-wide aggregation, the AMI method operates most effectively in summer months when solar generation is highest. Over 75% of all days within three years modeled are estimated to within ±20% with established methods. Advantages of the AMI model with regard to snow coverage, shading, and difficult to model factors are discussed, and next-day PV prediction using forecasted weather data is also explored. This work provides a foundation for disaggregating solar generation from AMI data, without knowing specific physical parameters of the array or using known generation for computational training.