Event Detection Using Linear Regression and Historical Weather Data

dc.contributor.advisor

Johnson, Timothy

dc.contributor.author

Garafola, Nicholas

dc.date.accessioned

2015-04-24T03:08:59Z

dc.date.available

2015-04-24T03:08:59Z

dc.date.issued

2015-04-23

dc.department

Nicholas School of the Environment and Earth Sciences

dc.description.abstract

The Research Triangle Institute (“RTI”) seeks to minimize water consumption by automating the process of detecting water and energy over- and under-consumption events associated with cooling systems. The Central Utility Plant (“CUP”), which serves roughly 25% of gross built area on RTI’s main campus, has contributed to past over-consumption events due to mechanical failure of cooling tower water makeup float valves. RTI’s facilities team would like to assemble data and examine the relationship between atmospheric conditions and water consumption. This project entails development of a data cleaning and analysis tool based in Microsoft Excel that allows RTI facilities and operations teams to periodically update a predictive model in response to changing facility parameters that are external to the model, including changes in building footprint, occupancy and HVAC settings. The final deliverable includes a user guide that explains the functions of the Excel tool as well as the limitations of predictions based on linear regression models.

dc.identifier.uri

https://hdl.handle.net/10161/9634

dc.language.iso

en_US

dc.subject

cooling

dc.subject

linear regression

dc.subject

event detection

dc.subject

building automation

dc.subject

HVAC

dc.subject

weather data

dc.title

Event Detection Using Linear Regression and Historical Weather Data

dc.type

Master's project

duke.embargo.months

0

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Garafola_2015_Event Detection Using Linear Regression and Historical Weather Data_FINAL.pdf
Size:
1.66 MB
Format:
Adobe Portable Document Format
Description:
Main article