Name
Abstract | ||
|---|---|---|
Occupancy prediction is a relatively new domain of research. It has gained momentum over the past decade. Varying approaches have been proposed to profile occupancy of buildings or space. Smart occupancy patterns, once predicted, can be effectively used in modeling energy management systems to achieve energy savings. While doing so, we also take into consideration the potential for occupant discomfort. In this paper, we propose DOPM - an occupancy prediction model built by using Decision Guidance Query Language (DGQL) framework that can optimize prediction rules governing occupancy patterns in a domain. Motive of DOPM is to perform two actions: a) Maximize energy saved in a location and b) limit inconvenience caused to its occupants in the process. This paper presents a generic DOPM model. A case study is developed for occupancy prediction on a university campus setting and the results of running the model will be presented. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/HICSS.2011.281 | HICSS |
Keywords | Field | DocType |
occupancy pattern,energy savings,learning occupancy prediction models,occupant discomfort,new domain,smart occupancy pattern,decision guidance query language,energy saving,smart occupancy patterns,occupancy prediction models,energy management system,decision support systems,energy management systems,occupancy prediction,decision-guidance query language,generic dopm model,prediction rule,occupancy prediction model,building management systems,energy management,query language,data models,optimization,data model,prediction model,schedules,predictive models,sensors | Data modeling,Data mining,Query language,Building management system,Computer science,Knowledge management,Occupancy,Artificial intelligence,Predictive modelling,Energy management,Decision support system,Schedule,Machine learning | Conference |
ISSN | ISBN | Citations |
1530-1605 | 978-1-4244-9618-1 | 1 |
PageRank | References | Authors |
0.43 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdullah Alrazgan | 1 | 1 | 0.43 |
| Ajay Nagarajan | 2 | 8 | 1.20 |
| Alexander Brodsky | 3 | 510 | 92.99 |
| Nathan Egge | 4 | 32 | 6.85 |