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  1. KEEP
  2. Theses and Dissertations
  3. Barrett, The Honors College Thesis/Creative Project Collection
  4. Data and Predictive Analytics for Energy Use
  5. Full metadata

Data and Predictive Analytics for Energy Use

Full metadata

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).

Date Created
2016-12
Contributors
  • Camarena, Raquel Jimenez (Author)
  • Chong, Oswald (Thesis director)
  • Ye, Nong (Committee member)
  • Industrial, Systems (Contributor)
  • Barrett, The Honors College (Contributor)
Topical Subject
  • energy
  • Industrial Engineering
  • Data Mining
Resource Type
Text
Extent
49 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Barrett, The Honors College Thesis/Creative Project Collection
Series
Academic Year 2016-2017
Handle
https://hdl.handle.net/2286/R.I.42866
Level of coding
minimal
Cataloging Standards
asu1
System Created
  • 2017-10-30 02:50:58
System Modified
  • 2021-07-15 10:18:27
  •     
  • 1 year 6 months ago
Additional Formats
  • OAI Dublin Core
  • MODS XML

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