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Development of Pre-Screening Indices to Improve Energy Analysis of Public K-12 Schools
|dc.creator||Landman, David Shea||en_US|
|dc.description||Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to firstname.lastname@example.org, referencing the URI of the item.||en_US|
|dc.description||Includes bibliographical references: p.183-186.||en_US|
|dc.description||Issued also on microfiche from Lange Micrographics.||en_US|
|dc.description.abstract||Indices are an important tool used to increase the accuracy and efficiency of the energy audit process. This thesis describes methods for using annual, monthly, daily, and hourly indices to improve current energy auditing processes. Eleven schools in different regions in Texas were identified for the case studies. The results show that certain indices match what is recommended by on-site visits and actually provide additional information that is sometimes not identified by a site visit. The indices developed provide a useful means by which energy audit firms and building owners/administrators can identify those areas of a building that have the most potential for energy cost reduction measures and operation and maintenance measures prior to a site visit. These indices assist the energy auditor in performing more efficient energy analyses on buildings. Each school in this thesis was audited prior to this study as part of the Texas LoanSTAR program. The indices were then developed using data from the period between September 1991 and December 1993. Retrofits to the case study buildings were completed during this period also. The sites were then reaudited to confirm the results from the previous audits, the usefulness of the indices, and/or discover new areas for energy savings. Two important new findings from this thesis are: 1) that schools are better modeled by grouping data into separate occupancy profiles consisting of school year months and summer months; and 2) the school year base-level electricity consumption can be calculated by taking the 25th percentiles of all twelve months of data reported. This approximately matches the base-level determined when running a 3-parameter cooling models on monthly energy consumption data and has the advantage that it does not require coincident weather data.||en_US|
|dc.publisher||Texas A&M University||en_US|
|dc.rights||This thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries in 2008. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use.||en_US|
|dc.subject||Major mechanical engineering.||en_US|
|dc.title||Development of Pre-Screening Indices to Improve Energy Analysis of Public K-12 Schools||en_US|
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