2014 IEEE International Conference on Pervasive Computing and Communications (PerCom) 2014
DOI: 10.1109/percom.2014.6813940
|View full text |Cite
|
Sign up to set email alerts
|

Using rule mining to understand appliance energy consumption patterns

Abstract: Managing energy in the home is key to creating a sustainable future for our society. More tools are increasingly available to measure home energy usage, however these tools provide little insight into questions such as why an appliance consumes more energy than normal or what kinds of behavioral changes might be most likely to reduce energy usage in the home. To answer these questions, a deeper understanding of the causal factors that influence energy usage is necessary. In this work, we conduct a broad study … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
11
0

Year Published

2014
2014
2019
2019

Publication Types

Select...
4
3
2

Relationship

1
8

Authors

Journals

citations
Cited by 32 publications
(11 citation statements)
references
References 31 publications
0
11
0
Order By: Relevance
“…The work in [41] presented the association rule mining method to classify the interdependence between power consumption and appliance usage to help power saving, anomaly detection, and demand response. Nevertheless, this work lacked the proper rule mining process and appliance-appliance association.…”
Section: Related Workmentioning
confidence: 99%
“…The work in [41] presented the association rule mining method to classify the interdependence between power consumption and appliance usage to help power saving, anomaly detection, and demand response. Nevertheless, this work lacked the proper rule mining process and appliance-appliance association.…”
Section: Related Workmentioning
confidence: 99%
“…The more we zoom into device usage scenarios, the more attributes need to be considered. Rather than reducing the complexity of the n-ary usage logs analysis into a frequent binary pattern and association rule mining problem by converting n-ary to binary logs (e.g., Context × Activity, Context × Device) [2], [4], [5], [12]- [14], we rely on recent advances in data mining over n-ary relations (e.g., Device × Context × Activity) [6], [7]. Our choice is motivated by the need to employ the same general purpose mining algorithms for serving different analytical use cases.…”
Section: Analysis Of Device Co-usagementioning
confidence: 99%
“…Traditional market basket analysis has been recently revised for extracting associations between users' interactions (e.g., communication and entertainment services) and context (e.g., time periods) captured by mobile devices [2], [3], frequent co-occurring mobile context events (e.g., a user listens to music during workdays, while driving) [4] or frequent co-usage patterns of different appliances under various contexts [5]. Unlike these works, we extract n-ary (vs. binary) patterns from device logs involving attributes of at least three distinct entities: Device, Context, and Activity.…”
Section: Introductionmentioning
confidence: 99%
“…We have prototypes of both a web application and an Android application. We have also tested a prototype that collects contextual information such as when the user is home and what the user is doing (activities like cooking, entertainment, chores, and work) when energy usage is high [18]. As future work, we plan to use this additional contextual information to inform our prediction algorithms.…”
Section: A Data Collection Systemmentioning
confidence: 99%