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Poster
Similarity measures in smart building electrical demand data
Poster presentation in based on a conference paper presented at the 2015 IEEE SoutheastCon and published in the conference proceedings.
Student Scholar Symposium and Faculty Research Showcase (University of West Florida, Pensacola, Florida, 2015)
2015
Abstract
With the increase in smart, LEED-certified buildings there comes an increase in the amount of time-series data generated by the sensor networks within these buildings. Extracting useful information from the sensor network data can pose a challenge. While diurnal and seasonal patterns of electrical demand are well known from traditional metering systems, smart building sensor networks can provide insight into abnormalities or previously unknown patterns in electrical demand. In this paper, we demonstrate how to mine the data for these unknowns through the analysis of the frequency components of the time-series electrical demand data. The data for this study was collected from an LEED-certified building over 12 consecutive months with separate feeds for the electrical demand from the heating, A/C, ventilation, lighting, and miscellaneous systems. We employed Fourier methods to transform the data from the time domain to the frequency domain and then used similarity measures to look for similarities and outliers among the differing systems.
Details
- Title
- Similarity measures in smart building electrical demand data
- Edition
- Poster presentation in based on a conference paper presented at the 2015 IEEE SoutheastCon and published in the conference proceedings.
- Resource Type
- Poster
- Event
- Student Scholar Symposium and Faculty Research Showcase (University of West Florida, Pensacola, Florida, 2015)
- Format
- pdf
- Copyright
- Permission granted to the University of West Florida Libraries by the author to digitize and/or display this information for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires the permission of the copyright holder.
- Identifiers
- 99380090324306600
- Academic Unit
- Computer Science; Hal Marcus College of Science and Engineering ; Cybersecurity and Information Technology
- Language
- English