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Data and Predictive Analytics for Energy Use

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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…

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
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Multi-Layer Optical Coatings Composed of Silicon Nanoparticles

Description
To compete with fossil fuel electricity generation, there is a need for higher efficiency solar cells to produce renewable energy. Currently, this is the best way to lower generation costs and the price of energy [1]. The goal of this Barrett Honors Thesis is to design an optical coating model…

To compete with fossil fuel electricity generation, there is a need for higher efficiency solar cells to produce renewable energy. Currently, this is the best way to lower generation costs and the price of energy [1]. The goal of this Barrett Honors Thesis is to design an optical coating model that has five or fewer layers (with varying thickness and refractive index, within the above range) and that has the maximum reflectance possible between 950 and 1200 nanometers for normally incident light. Manipulating silicon monolayers to become efficient inversion layers to use in solar cells aligns with the Ira. A Fulton Schools of Engineering research themes of energy and sustainability [2]. Silicon monolayers could be specifically designed for different doping substrates. These substrates could range from common-used materials such as boron and phosphorus, to rare-earth doped zinc oxides or even fullerene blends. Exploring how the doping material, and in what quantity, affects solar cell energy output could revolutionize the current production methods and commercial market. If solar cells can be manufactured more economically, yet still retain high efficiency rates, then more people will have access to alternate, "green" energy that does not deplete nonrenewable resources.
ContributorsSanford, Kari Paige (Author) / Holman, Zachary (Thesis director) / Weigand, William (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12